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Traditionally, motion sickness symptom reduction has implied use of medication, which can lead to detrimental effects on performance. Non-pharmaceutical strategies, in turn, often require cognitive and perceptual attention. Hence, for people working in high demand environments where it is impossible to reallocate focus of attention, other strategies are called upon. The aim of the study was to investigate possible impact of a mitigation strategy on perceived motion sickness and psychophysiological responses, based on an artificial sound horizon compared with a non-positioned sound source. Twenty-three healthy subjects were seated on a motion platform in an artificial sound horizon or in non-positioned sound, in random order with one week interval between the trials. Perceived motion sickness (Mal), maximum duration of exposure (ST), skin conductance, blood volume pulse, temperature, respiration rate, eye movements and heart rate were measured continuously throughout the trials. Mal scores increased over time in both sound conditions, but the artificial sound horizon, applied as a mitigation strategy for perceived motion sickness, showed no significant effect on Mal scores or ST. The number of fixations increased with time in the non-positioned sound condition. Moreover, fixation time was longer in the non-positioned sound condition compared with sound horizon, indicating that the subjects used more time to fixate and, hence, assumingly made fewer saccades. A subliminally presented artificial sound horizon did not significantly affect perceived motion sickness, psychophysiological variables or the time the subjects endured the motion sickness triggering stimuli. 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New York: Plenum press; 1967.",{"doi":287},{"id":283,"text":401,"url":285,"identifiers":402},"Dahlman J, Sjörs A, Lindström J, Ledin T, Falkmer T: Performance and autonomic responses during motion sickness. Hum Factors 2008, in press.",{"doi":287},{"id":283,"text":404,"url":285,"identifiers":405},"Kennedy RS, Stanney KM, Rolland J, Ordy MJ, Mead AP: Motion sickness symptoms and perception of self motion from exposure to different wallpaper patterns. Human Factors And Ergonomics Society 46th Annual Meeting; September 30-October 4; Pittsburgh, PA 2002, 2129-2133.",{"doi":287},{"id":407,"text":408,"url":409,"identifiers":410},"e2582dd5-4b9b-4e61-80a8-13f552f9e3ca","Golding JF: Motion Sickness Susceptibility. Auton Neurosci 2006, 67-76. 10.1016\u002Fj.autneu.2006.07.019","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1566070206002128",{"doi":411},"10.1016\u002Fj.autneu.2006.07.019",false,{"id":414,"createTime":415,"updateTime":416,"relativeEntities":417,"slug":418,"properties":419,"entityType":135,"verifyStatus":136,"verifyTime":430,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":431,"fullTextUrl":20,"authors":432,"publicationType":214,"publisherRelationship":488,"citationCount":21,"citationInfo":539,"publishDate":542,"publishYear":540,"citationAnalyzeStatus":271,"lastCitationAnalyze":416,"indexDatabases":543,"openAccess":20,"references":20,"isForceReanalyzing":412},"281c277b-f919-4e77-8136-cfca2a0b321a","2024-01-04T14:06:33.949+00:00","2026-08-17T02:36:06.151+00:00",[],"Towards-functional-robotic-training-motor-learning-of-dynamic-tasks-is-enhanced-by-haptic-rendering-but-hampered-by-arm-weight-support",{"abstract":420,"title":422,"gsPaper":424,"references":426,"doi":428},{"EN":421},"Current robot-aided training allows for high-intensity training but might hamper the transfer of learned skills to real daily tasks. Many of these tasks, e.g., carrying a cup of coffee, require manipulating objects with complex dynamics. Thus, the absence of somatosensory information regarding the interaction with virtual objects during robot-aided training might be limiting the potential benefits of robotic training on motor (re)learning. We hypothesize that providing somatosensory information through the haptic rendering of virtual environments might enhance motor learning and skill transfer. Furthermore, the inclusion of haptic rendering might increase the task realism, enhancing participants’ agency and motivation. Providing arm weight support during training might also enhance learning by limiting participants’ fatigue. We conducted a study with 40 healthy participants to evaluate how haptic rendering and arm weight support affect motor learning and skill transfer of a dynamic task. The task consisted of inverting a virtual pendulum whose dynamics were haptically rendered on an exoskeleton robot designed for upper limb neurorehabilitation. Participants trained with or without haptic rendering and with or without weight support. Participants’ task performance, movement strategy, effort, motivation, and agency were evaluated during baseline, short- and long-term retention. We also evaluated if the skills acquired during training transferred to a similar task with a shorter pendulum. We found that haptic rendering significantly increases participants’ movement variability during training and the ability to synchronize their movements with the pendulum, which is correlated with better performance. Weight support also enhances participants’ movement variability during training and reduces participants’ physical effort. Importantly, we found that training with haptic rendering enhances motor learning and skill transfer, while training with weight support hampers learning compared to training without weight support. We did not observe any significant differences between training modalities regarding agency and motivation during training and retention tests. Haptic rendering is a promising tool to boost robot-aided motor learning and skill transfer to tasks with similar dynamics. However, further work is needed to find how to simultaneously provide robotic assistance and haptic rendering without hampering motor learning, especially in brain-injured patients. \n                  Trial registration\n                  \n                    https:\u002F\u002Fclinicaltrials.gov\u002Fshow\u002FNCT04759976\n                    \n                  \n                ",{"EN":423},"Towards functional robotic training: motor learning of dynamic tasks is enhanced by haptic rendering but hampered by arm weight support",{"VOID":425},"[\"4701392068224406376\"]",{"VOID":427},"Feigin VL, et al. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):439–58. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1474-4422(19)30034-1.\nBayona NA, Bitensky J, Salter K, Teasell R. The role of task-specific training in rehabilitation therapies. 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Neuromuscular electrical stimulation (NMES) and transcutaneous electrical nerve stimulation (TENS) are two widely used interventions to reduce pain, but the comparative efficacy of these two modalities remains uncertain. The purpose of this research was to compare the immediate and retained effects of EMG-triggered NMES and TENS, both in combination with bilateral arm training, on hemiplegic shoulder pain and arm function of stroke patients. A single-blind, randomized controlled trial was conducted at two medical centers. Thirty-eight patients (25 males and 13 females, 60.75 ± 10.84 years old, post stroke duration 32.68 ± 53.07 months) who had experienced a stroke more than 3 months ago at the time of recruitment and hemiplegic shoulder pain were randomized to EMG-triggered NMES or TENS. Both groups received electrical stimulation followed by bilateral arm training 3 times a week for 4 weeks. The primary outcome measures included a vertical Numerical Rating Scale supplemented with a Faces Rating Scale, and the short form of the Brief Pain Inventory. The secondary outcome measures were the upper-limb subscale of the Fugl-Meyer Assessment, and pain-free passive shoulder range of motion. All outcomes were measured pretreatment, post-treatment, and at 1-month after post-treatment. Two-way mixed repeated measures ANOVAs were used to examine treatment effects. Compared to TENS with bilateral arm training, the EMG-triggered NMES with bilateral arm training was associated with lower pain intensity during active and passive shoulder movement (P =0.007, P =0.008), lower worst pain intensity (P = 0.003), and greater pain-free passive shoulder abduction (P =0.001) and internal rotation (P =0.004) at follow-up. Both groups improved in pain at rest (P =0.02), pain interference with daily activities, the Fugl-Meyer Assessment, and pain-free passive shoulder flexion and external rotation post-treatment (P \u003C 0.001) and maintained the improvement at follow-up (P \u003C 0.001), except for resting pain (P =0.08). EMG-triggered NMES with bilateral arm training exhibited greater immediate and retained effects than TENS with bilateral arm training with respect to pain and shoulder impairment for chronic and subacute stroke patients with hemiplegic shoulder pain. \n                           \n                    NCT01913509\n                    \n                  .",{"EN":554},"Effect of EMG-triggered neuromuscular electrical stimulation with bilateral arm training on hemiplegic shoulder pain and arm function after stroke: a randomized controlled 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Shoulder pain after stroke: a prospective population-based study. Stroke. 2007;38:343–8.",{},{"id":283,"text":787,"url":285,"identifiers":788},"Sheffler LR, Chae J. Neuromuscular electrical stimulation in neurorehabilitation. Muscle Nerve. 2007;35:562–90.",{"doi":287},{"id":283,"text":790,"url":285,"identifiers":791},"Viana R, Pereira S, Mehta S, Miller T, Teasell R. Evidence for therapeutic interventions for hemiplegic shoulder pain during the chronic stage of stroke: a review. Top Stroke Rehabil. 2012;19:514–22.",{"doi":287},{"id":283,"text":793,"url":285,"identifiers":794},"Snels IA, Dekker JH, van der Lee JH, Lankhorst GJ, Beckerman H, Bouter LM. Treating patients with hemiplegic shoulder pain. Am J Phys Med Rehabil. 2002;81:150–60.",{"doi":287},{"id":20,"text":796,"url":20,"identifiers":797},"Lakse E, Gunduz B, Erhan B, Celik EC. The effect of local injections in hemiplegic shoulder pain: a prospective, randomized, controlled study. Am J Phys Med Rehabil. 2009;88:805–11. quiz 812-804, 851",{},{"id":20,"text":799,"url":20,"identifiers":800},"de Kroon JR, Ijzerman MJ, Chae J, Lankhorst GJ, Zilvold G. Relation between stimulation characteristics and clinical outcome in studies using electrical stimulation to improve motor control of the upper extremity in stroke. J Rehabil Med. 2005;37:65–74.",{},{"id":283,"text":802,"url":285,"identifiers":803},"Chae J, Sheffler L, Knutson J. Neuromuscular electrical stimulation for motor restoration in hemiplegia. Top Stroke Rehabil. 2008;15:412–26.",{"doi":287},{"id":283,"text":805,"url":285,"identifiers":806},"Price CI, Pandyan AD. Electrical stimulation for preventing and treating post-stroke shoulder pain: a systematic Cochrane review. Clin Rehabil. 2001;15:5–19.",{"doi":287},{"id":283,"text":808,"url":285,"identifiers":809},"Doucet BM, Lam A, Griffin L. Neuromuscular electrical stimulation for skeletal muscle function. Yale J Biol Med. 2012;85:201–15.",{"doi":287},{"id":283,"text":811,"url":285,"identifiers":812},"IJzerman MJ, Renzenbrink GJ, Geurts AC. Neuromuscular stimulation after stroke: from technology to clinical deployment. Expert Rev Neurother. 2009;9:541–52.",{"doi":287},{"id":283,"text":814,"url":285,"identifiers":815},"Baker LL, Parker K. Neuromuscular electrical stimulation of the muscles surrounding the shoulder. Phys Ther. 1986;66:1930–7.",{"doi":287},{"id":817,"text":818,"url":819,"identifiers":820},"3ca8a65e-bc39-4638-803a-adef640f24a9","Chantraine A, Baribeault A, Uebelhart D, Gremion G. Shoulder pain and dysfunction in hemiplegia: effects of functional electrical stimulation. Arch Phys Med Rehabil. 1999;80:328–31.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0003999399901466",{"doi":821},"10.1016\u002Fs0003-9993(99)90146-6",{"id":283,"text":823,"url":285,"identifiers":824},"Church C, Price C, Pandyan AD, Huntley S, Curless R, Rodgers H. Randomized controlled trial to evaluate the effect of surface neuromuscular electrical stimulation to the shoulder after acute stroke. Stroke. 2006;37:2995–3001.",{"doi":287},{"id":283,"text":826,"url":285,"identifiers":827},"Kobayashi H, Onishi H, Ihashi K, Yagi R, Handa Y. Reduction in subluxation and improved muscle function of the hemiplegic shoulder joint after therapeutic electrical stimulation. J Electromyogr Kinesiol. 1999;9:327–36.",{"doi":287},{"id":283,"text":829,"url":285,"identifiers":830},"Koyuncu E, Nakipoglu-Yuzer GF, Dogan A, Ozgirgin N. The effectiveness of functional electrical stimulation for the treatment of shoulder subluxation and shoulder pain in hemiplegic patients: a randomized controlled trial. Disabil Rehabil. 2010;32:560–6.",{"doi":287},{"id":283,"text":832,"url":285,"identifiers":833},"Linn SL, Granat MH, Lees KR. Prevention of shoulder subluxation after stroke with electrical stimulation. Stroke. 1999;30:963–8.",{"doi":287},{"id":283,"text":835,"url":285,"identifiers":836},"Wang RY, Yang YR, Tsai MW, Wang WT, Chan RC. Effects of functional electric stimulation on upper limb motor function and shoulder range of motion in hemiplegic patients. Am J Phys Med Rehabil. 2002;81:283–90.",{"doi":287},{"id":838,"text":839,"url":840,"identifiers":841},"d771c2f2-05ff-49bc-a385-d2dfff7cd177","Faghri PD, Rodgers MM, Glaser RM, Bors JG, Ho C, Akuthota P. The effects of functional electrical stimulation on shoulder subluxation, arm function recovery, and shoulder pain in hemiplegic stroke patients. Arch Phys Med Rehabil. 1994;75:73–9.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F0003999394903417",{"doi":842},"10.1016\u002F0003-9993(94)90341-7",{"id":283,"text":844,"url":285,"identifiers":845},"Kalichman L, Ratmansky M. Underlying pathology and associated factors of hemiplegic shoulder pain. Am J Phys Med Rehabil. 2011;90:768–80.",{"doi":287},{"id":283,"text":847,"url":285,"identifiers":848},"Ratnasabapathy Y, Broad J, Baskett J, Pledger M, Marshall J, Bonita R. Shoulder pain in people with a stroke: a population-based study. Clin Rehabil. 2003;17:304–11.",{"doi":287},{"id":283,"text":850,"url":285,"identifiers":851},"Mangold S, Schuster C, Keller T, Zimmermann-Schlatter A, Ettlin T. Motor training of upper extremity with functional electrical stimulation in early stroke rehabilitation. Neurorehabil Neural Repair. 2009;23:184–90.",{"doi":287},{"id":283,"text":853,"url":285,"identifiers":854},"Chan MK, Tong RK, Chung KY. Bilateral upper limb training with functional electric stimulation in patients with chronic stroke. Neurorehabil Neural Repair. 2009;23:357–65.",{"doi":287},{"id":20,"text":856,"url":20,"identifiers":857},"Cauraugh JH, Kim S. Two coupled motor recovery protocols are better than one: electromyogram-triggered neuromuscular stimulation and bilateral movements. Stroke. 2002;33:1589–94.",{},{"id":859,"text":860,"url":861,"identifiers":862},"80227531-11a4-4ed9-a1b0-fcf17589ebce","Cauraugh JH, Kim SB, Duley A. Coupled bilateral movements and active neuromuscular stimulation: intralimb transfer evidence during bimanual aiming. Neurosci Lett. 2005;382:39–44.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0304394005002545",{"doi":863},"10.1016\u002Fj.neulet.2005.02.060",{"id":20,"text":865,"url":20,"identifiers":866},"Basmajian JV, Bazant FJ. Factors preventing downward dislocation of the adducted shoulder joint. An electromyographic and morphological study. J Bone Joint Surg Am. 1959;41-A:1182–6.",{},{"id":283,"text":868,"url":285,"identifiers":869},"Paci M, Nannetti L, Rinaldi LA. Glenohumeral subluxation in hemiplegia: an overview. J Rehabil Res Dev. 2005;42:557–68.",{"doi":287},{"id":20,"text":871,"url":20,"identifiers":872},"Delagi EF, Perotto AO, Iazzetti J, Morrison D. Shoulder joint. In: Perotto AO, Springfield IL, Thomas CC, editors. Anatomical guide for the Electromyographer: the limbs and trunk. Fifth ed; 2011. p. 117–37.",{},{"id":20,"text":874,"url":20,"identifiers":875},"Aras MD, Gokkaya NK, Comert D, Kaya A, Cakci A. Shoulder pain in hemiplegia: results from a national rehabilitation hospital in Turkey. Am J Phys Med Rehabil. 2004;83:713–9.",{},{"id":283,"text":877,"url":285,"identifiers":878},"Murie-Fernandez M, Carmona Iragui M, Gnanakumar V, Meyer M, Foley N, Teasell R. Painful hemiplegic shoulder in stroke patients: causes and management. Neurologia. 2012;27:234–44.",{"doi":287},{"id":283,"text":880,"url":285,"identifiers":881},"Chuang LL, CY W, Lin KC, Hsieh CJ. Relative and absolute reliability of a vertical numerical pain rating scale supplemented with a faces pain scale after stroke. 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Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys Ther. 1983;63:1606–10.",{"doi":287},{"id":283,"text":898,"url":285,"identifiers":899},"Bohannon RW, Larkin PA, Smith MB, Horton MG. Shoulder pain in hemiplegia: statistical relationship with five variables. Arch Phys Med Rehabil. 1986;67:514–6.",{"doi":287},{"id":901,"text":902,"url":903,"identifiers":904},"15ed9fc4-df49-4f19-83da-26af476c8e4f","Faul F, Erdfelder E, Lang AG, Buchner A. G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.3758\u002FBF03193146",{"doi":905},"10.3758\u002FBF03193146",{"id":283,"text":907,"url":285,"identifiers":908},"Bohannon RW, Andrews AW. Shoulder subluxation and pain in stroke patients. 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The effects of bilateral arm training on motor control and functional performance in chronic stroke: a randomized controlled study. Neurorehabil Neural Repair. 2010;24:42–51.",{"doi":287},{"id":283,"text":922,"url":285,"identifiers":923},"Stoykov ME, Corcos DM. A review of bilateral training for upper extremity hemiparesis. Occup Ther Int. 2009;16:190–203.",{"doi":287},{"id":283,"text":925,"url":285,"identifiers":926},"Page SJ, Fulk GD, Boyne P. Clinically important differences for the upper-extremity Fugl-Meyer scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther. 2012;92:791–8.",{"doi":287},{"id":283,"text":928,"url":285,"identifiers":929},"Wu FC, Lin YT, Kuo TS, Luh JJ, Lai JS. Clinical effects of combined bilateral arm training with functional electrical stimulation in patients with stroke. IEEE Int Conf Rehabil Robot. 2011;2011:5975367.",{"doi":287},{"id":283,"text":931,"url":285,"identifiers":932},"Lin KC, Chang YF, CY W, Chen YA. Effects of constraint-induced therapy versus bilateral arm training on motor performance, daily functions, and quality of life in stroke survivors. Neurorehabil Neural Repair. 2009;23:441–8.",{"doi":287},{"id":283,"text":934,"url":285,"identifiers":935},"McCombe Waller S, Forrester L, Villagra F, Whitall J. Intracortical inhibition and facilitation with unilateral dominant, unilateral nondominant and bilateral movement tasks in left- and right-handed adults. J Neurol Sci. 2008;269:96–104.",{"doi":287},{"id":283,"text":937,"url":285,"identifiers":938},"Cauraugh JH, Kim SB. Chronic stroke motor recovery: duration of active neuromuscular stimulation. J Neurol Sci. 2003;215:13–9.",{"doi":287},{"id":940,"createTime":941,"updateTime":942,"relativeEntities":943,"slug":944,"properties":945,"entityType":135,"verifyStatus":136,"verifyTime":962,"verifyNote":138,"languages":963,"translateLanguages":20,"viewCount":21,"primaryUrl":965,"fullTextUrl":20,"authors":966,"publicationType":214,"publisherRelationship":1106,"citationCount":688,"citationInfo":1152,"publishDate":1155,"publishYear":1153,"citationAnalyzeStatus":19,"lastCitationAnalyze":1156,"indexDatabases":1157,"openAccess":20,"references":1158,"isForceReanalyzing":412},"b598f9df-2872-4ccf-8157-53c5d3771295","2024-04-15T07:10:14.482+00:00","2026-07-27T07:44:00.263+00:00",[],"Impairments-of-cortico-cortical-connectivity-in-fine-tactile-sensation-after-stroke",{"mag":946,"gsPaper":948,"pmc":950,"openalex":952,"abstract":954,"title":956,"pm":958,"doi":960},{"VOID":947},"3132034827",{"VOID":949},"[\"8622387566865360299\"]",{"VOID":951},"7885375",{"VOID":953},"W3132034827",{"EN":955},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:sec>\n                \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>Fine tactile sensation plays an important role in motor relearning after stroke. However, little is known about its dynamics in post-stroke recovery, principally due to a lack of effective evaluation on neural responses to fine tactile stimulation. This study investigated the post-stroke alteration of cortical connectivity and its functional structure in response to fine tactile stimulation via textile fabrics by electroencephalogram (EEG)-derived functional connectivity and graph theory analyses.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Method\u003C\u002Fjats:title>\n                \u003Cjats:p>Whole brain EEG was recorded from 64 scalp channels in 8 participants with chronic stroke and 8 unimpaired controls before and during the skin of the unilateral forearm contacted with a piece of cotton fabric. Functional connectivity (FC) was then estimated using EEG coherence. The fabric stimulation induced FC (SFC) was analyzed by a cluster-based permutation test for the FC in baseline and fabric stimulation. The functional structure of connectivity alteration in the brain was also investigated by assessing the multiscale topological properties of functional brain networks according to the graph theory.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>In the SFC distribution, an altered hemispheric lateralization (HL) (HL degree, 14%) was observed when stimulating the affected forearm in the stroke group, compared to stimulation of the unaffected forearm of the stroke group (HL degree, 53%) and those of the control group (HL degrees, 92% for the left and 69% for the dominant right limb). The involvement of additional brain regions, i.e., the distributed attention networks, was also observed when stimulating either limb of the stroke group compared with those of the control. Significantly increased (P < 0.05) global and local efficiencies were found when stimulating the affected forearm compared to the unaffected forearm. A significantly increased (P < 0.05) degree of inter-hemisphere FC (interdegree) mainly within ipsilesional somatosensory region and a significantly diminished degree of intra-hemisphere FC (intradegree) (P < 0.05) in ipsilesional primary somatosensory region were observed when stimulating the affected forearm, compared with the unaffected forearm.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>The alteration of cortical connectivity in fine tactile sensation post-stroke was characterized by the compensation from the contralesional hemisphere and distributed attention networks related to involuntary attention. The interhemispheric connectivity could implement the compensation from the contralateral hemisphere to the ipsilesional somatosensory region. Stroke participants also exerted increased cortical activities in fine tactile sensation.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":957},"Impairments of cortico-cortical connectivity in fine tactile sensation after stroke",{"VOID":959},"33588877",{"VOID":961},"10.1186\u002Fs12984-021-00821-7","2024-06-25T00:51:38.955+00:00",[964],"EN","https:\u002F\u002Fjneuroengrehab.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12984-021-00821-7",[967,986,1001,1022,1039,1054,1071,1088],{"id":968,"sortIndex":21,"researcher":20,"roles":969,"affiliations":970,"properties":979,"displayName":983,"givenName":20,"familyName":20},"b80c2a09-3cf3-42f2-bb9d-c0d0cdc0165c",[],[971],{"id":972,"sortIndex":21,"affiliation":973,"properties":20},"acff7a31-c537-4e28-86c6-f1d29d531ec8",{"id":972,"createTime":20,"updateTime":20,"relativeEntities":974,"slug":20,"properties":975,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":978,"statistic":20},[],{"title":976},{"VI":977},"Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, 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Neurorehabil Neural Repair. 2011;25(5):443–57.",{"doi":1383},"10.1177\u002F1545968310395777",{"id":20,"text":1385,"url":20,"identifiers":1386},"Volz LJ, Sarfeld A-S, Diekhoff S, Rehme AK, Pool E-M, Eickhoff SB, Fink GR, Grefkes C. Motor cortex excitability and connectivity in chronic stroke: a multimodal model of functional reorganization. Brain Struct Funct. 2015;220(2):1093–107.",{"doi":1387},"10.1007\u002Fs00429-013-0702-8",{"id":20,"text":1389,"url":20,"identifiers":1390},"Liu J, Qin W, Zhang J, Zhang X, Yu C. Enhanced interhemispheric functional connectivity compensates for anatomical connection damages in subcortical stroke. Stroke. 2015;46(4):1045–51.",{"doi":1391},"10.1161\u002FSTROKEAHA.114.007044",{"id":20,"text":1393,"url":20,"identifiers":1394},"Grefkes C, Nowak DA, Eickhoff SB, Dafotakis M, Küst J, Karbe H, Fink GR. Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. 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Cognitive effort drives workspace configuration of human brain functional networks. J Neurosci. 2011;31(22):8259–70.",{"doi":1411},"10.1523\u002FJNEUROSCI.0440-11.2011",{"id":20,"text":1413,"url":20,"identifiers":1414},"Sullivan KJ, Tilson JK, Cen SY, Rose DK, Hershberg J, Correa A, Gallichio J, McLeod M, Moore C, Wu SS. Fugl-Meyer assessment of sensorimotor function after stroke: standardized training procedure for clinical practice and clinical trials. Stroke. 2011;42(2):427–32.",{"doi":1415},"10.1161\u002FSTROKEAHA.110.592766",{"id":20,"text":1417,"url":20,"identifiers":1418},"Dodds TA, Martin DP, Stolov WC, Deyo RA. A validation of the functional independence measurement and its performance among rehabilitation inpatients. Arch Phys Med Rehabil. 1993;74(5):531–6.",{"doi":1419},"10.1016\u002F0003-9993(93)90119-U",{"id":20,"text":1421,"url":20,"identifiers":1422},"Yozbatiran N, Der-Yeghiaian L, Cramer SC. A standardized approach to performing the action research arm test. Neurorehabil Neural Repair. 2008;22(1):78–90.",{"doi":1423},"10.1177\u002F1545968307305353",{"id":1425,"createTime":1426,"updateTime":1427,"relativeEntities":1428,"slug":1429,"properties":1430,"entityType":135,"verifyStatus":136,"verifyTime":1441,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1442,"fullTextUrl":20,"authors":1443,"publicationType":214,"publisherRelationship":1476,"citationCount":1526,"citationInfo":1527,"publishDate":1530,"publishYear":540,"citationAnalyzeStatus":1531,"lastCitationAnalyze":1532,"indexDatabases":1533,"openAccess":20,"references":20,"isForceReanalyzing":412},"6a01763d-36ed-492b-8158-37d49f501946","2024-01-22T07:54:52.226+00:00","2026-07-25T23:01:18.208+00:00",[],"Deep-learning-approach-to-estimate-foot-pressure-distribution-in-walking-with-application-for-a-cost-effective-insole-system",{"abstract":1431,"title":1433,"gsPaper":1435,"references":1437,"doi":1439},{"EN":1432},"Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole. Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation. Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%. This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.",{"EN":1434},"Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system",{"VOID":1436},"[\"4482528358809585505\"]",{"VOID":1438},"Razak AH, Zayegh A, Begg RK, Wahab B. Foot plantar pressure measurement system: a review. Sensors. 2012;12:9884–912.\nRodgers MM. Dynamic foot biomechanics. J Orthop Sports Phys Ther. 1995;21:306–16.\nBrachman A, Sobota G, Marszalek W, Pawlowski M, Juras G, Bacik B. Plantar pressure distribution and spatiotemporal gait parameters after of radial shock wave therapy in patients with chronic plantar fasciitis. J Biomech. 2020;105:109773.\nNeri SGR, Gadelha AB, Correia ALM, Pereira JC, de David AC, Lima RM. Obesity is associated with altered plantar pressure distribution in older women. J Appl Biomech. 2017;33:323–9.\nKo M, Hughes L, Lewis H. Walking speed and peak plantar pressure distribution during barefoot walking in persons with diabetes. Physiother Res Int. 2012;17:29–35.\nGerlach C, Krumm D, Illing M, Lange J, Kanoun O, Odenwald S, Hubler A. Printed MWCNT-PDMS-composite pressure sensor system for plantar pressure monitoring in ulcer prevention. IEEE Sens J. 2015;15:3647–56.\nHessert MJ, Vyas M, Leach J, Hu K, Lipsitz LA, Novak V. Foot pressure distribution during walking in young and old adults. BMC Geriatr. 2005;5:5–8.\nSacco IC, Hamamoto AN, Tonicelli LM, Watari R, Ortega NR, Sartor CD. Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy. Gait Posture. 2014;40:570–74.\nJeffcoate WJ, Harding KG. Diabetic foot ulcers. Lanset. 2003;361:1545–51.\nRamirez-Bautista JA, Herta-Ruelas JA, Chaparro-Cardenas SL, Hernandez-Zavala A. A review in detection and monitoring gait disorders using in-shoe plantar measurement systms. IEEE Review Biomed Eng. 2017;10:299–309.\nOrlin MN, McPoil TG. Plantar pressure assessment. Phys Ther. 2000;80:399–409.\nChevalier TL, Hodgins H, Chockalingam N. Plantar pressure measurements using an in-shoe system and a pressure platform: a comparison. Gait Posture. 2010;31:397–9.\nStewart S, Dalbeth N, Vandal AC, Rome K. Spatiotemporal gait parameters and plantar pressure distribution during barefoot walking in people with gout and asymptomatic hyperuricemia: comparison with healthy individuals with normal serum urate concentrations. J Foot Ankle Res. 2016;9:1–9.\nGrecco LA, Tomita SM, Christovao TC, Pasini H, Sampaio LM, Oliveira CS. Effect of treadmill gait training on static and functional balance in children with cerebral palsy: a randomized controlled trial. Braz J Phys Ther. 2013;17:17–23.\nZammit GV, Menz HB, Munteanu SE, Landorf KB. Plantar pressure distribution in older people with osteoarthritis of the first metatarsophalangeal joint (hallux limitus\u002Frigidus). J Orthop Res. 2008;26:1665–9.\nChoi A, Jung H, Mun JH. Single inertial sensor-based neural networks to estimate COM-COP inclination angle during walking. Sensors. 2019;19:2974.\nSaito M, Nakajima K, Takano K, et al. An in-shoe device to measure plantar pressure during daily human activity. Med Eng Phys. 2011;33:638–45.\nValentini FA, Granger B, Hennebelle DS, Eythrib N, Robain G. Repeatability and variability of baropodometric and spatio-temporal gait parameters – results in healthy subjects and in stroke patients. Neurophysiol Clin-Clin Neurophysiol. 2011;41:181–9.\nHurkmans HL, Bussmann JB, Benda E, Verhaar JA, Stam HJ. Techniques for measuring weight bearing during standing and walking. Clin Biomech. 2003;18:576–89.\nVilarinho D, Theodosiou A, Leitao C, et al. POFBG-embedded cork insole for plantar pressure monitoring. Sensors. 2017;17:2924.\nSchollhorn WI. Applications of artificial neural nets in clinical biomechanics. Clin Biomech. 2004;19:876–98.\nVarrecchia T, De Marchis C, Rinaldi M, et al. Lifing activity assessment using surface electromyographic features and neural networks. Int J Ind Ergon. 2018;66:1–9.\nMehrizi R, Peng X, Zhang S, Li K. A deep neural network-based method for estimation of 3D lifting motions. J Biomech. 2019;84:87–93.\nLecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.\nSim T, Kwon H, Oh SE, et al. Predicting complete ground reaction forces and moments during gait with insole plantar pressure information using a wavelet neural network. J Biomech Eng. 2015;137:1.\nChoi A, Jung H, Lee KY, Lee S, Mun JH. Machine learning approach to predict center of pressure trajectories in a complete gait cycle: a feedforward neural network vs. LSTM network. Med Biol Eng Comput. 2019;57:2693–703.\nKim S, Nussbaum MA. Evaluation of two approaches for aligning data obtained from a motion capture system and an in-shoe pressure measurement system. Sensors. 2014;14:16994–7007.\nRouhani H, Favre J, Crevoisier X, Aminian K. Ambulatory assessment of 3D ground reaction force using plantar pressure distribution. Gait Posture. 2010;32:311–6.\nHowell AM, Kobayashi T, Hayes HA, Foreman KB, Bamberg SJ. Kinetic gait analysis using a low-cost insole. IEEE Trans Biomed Eng. 2013;60:3284–90.\nClaverie L, IIIe A, Moretto P. Validation of a method for dispatching discrete sensors on an insole for plantar pressure analysis. Comput Methods Biomech Biomed Eng. 2015;18:1908–99.\nChoi A, Jung H, Kim H, Mun JH. Predicting center of gravity displacement during walking using a single inertial sensor and deep learning technique. J Med Imaging Health Inform. 2020;10:1436–43.\nHu B, Dixon PC, Jacobs JV, Dennerlein JT, Schiffman JM. Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking. J Biomech. 2018;71:37–42.\nWu H, Huang Q, Wang D, Gao L. A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyo Kinesiol. 2018;42:136–42.\nGholipour A, Arjmand N. Artificial neural networks to predict 3D spinal posture in reaching and lifting activities; Applications in biomechanical models. J Biomech. 2016;49:2946–52.\nChoi A, Lee JM, Mun JH. Ground reaction forces predicted by using artificial neural network during asymmetric movements. Int J Precis Eng Manuf. 2013;14:475–83.\nNosratabadi S, Ardabili S, Lakner Z, Make C, Mosavi A. Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture. 2021;11:408.\nSahin M, Erol R. A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Math Comput Appl. 2017;22:43.\nChiu SL. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst. 1994;2:267–8.\nMensah RA, Xiao J, Das O, et al. Application of adaptive neuro-fuzzy inference system in flammability parameter prediction. Polymers. 2020;12:122.\nCristiani AM, Bertolotti GM, Marenzi E, Ramat S. An instrumented insole for long term monitoring movement, comfort, and ergonomics. IEEE Sens J. 2014;14:1564–72.\nDeschamps K, Birch I, McInnes J, Desloovere K, Matricali GA. Inter- and intra-observer reliability of masking in plantar pressure measurement analysis. Gait Posture. 2009;30:379–82.\nShu L, Hua T, Wang Y, Li Q, Feng DD, Tao X. In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array. IEEE Tran Inform Technol Biomed. 2010;14:767–75.\nCavanagh PR, Hewitt FG, Perry JE. In-shoe plantar pressure measurement: a review. The Foot. 1992;2:185–94.\nChoi A, Yun TS, Suh SW, et al. Determination of input variables for the development of a gait asymmetry expert system in patients with idiopathic scoliosis. Int J Precis Eng Manuf. 2013;14:811–8.\nArdestani MM, Zhang X, Wang L, et al. Human lower extremity joint moment prediction: A wavelet neural network approach. Expert Syst Appl. 2014;41:4422–33.\nLiu MM, Herzog W, Savelberg HH. Dynamic muscle force prediction from EMG: an artificial neural network approach. J Electromyogr Kinesiol. 1999;9:391–400.",{"VOID":1440},"10.1186\u002Fs12984-022-00987-8","2024-06-24T23:00:22.410+00:00","https:\u002F\u002Fjneuroengrehab.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12984-022-00987-8",[1444,1461],{"id":1445,"sortIndex":21,"researcher":20,"roles":1446,"affiliations":1447,"properties":1456,"displayName":1458,"givenName":20,"familyName":20},"87749c9d-494d-477f-8507-488c8f2bb6a6",[144],[1448],{"id":1449,"sortIndex":21,"affiliation":1450,"properties":20},"8450295e-50a1-431d-b424-0cdcf775a18b",{"id":1449,"createTime":20,"updateTime":20,"relativeEntities":1451,"slug":20,"properties":1452,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1455,"statistic":20},[],{"title":1453},{"VI":1454},"College of Medicine, The Pennsylvania State University, Hershey, USA",[],{"title":1457,"gsAuthor":1459},{"VI":1458},"Frederick Mun",{"VOID":1460},"[\"PDWZs80AAAAJ\"]",{"id":1462,"sortIndex":114,"researcher":20,"roles":1463,"affiliations":1464,"properties":1473,"displayName":1475,"givenName":20,"familyName":20},"a89f982d-f333-4cad-ab42-7c8edb738223",[144],[1465],{"id":1466,"sortIndex":21,"affiliation":1467,"properties":20},"8b2ea6cb-1466-4fa2-9a7a-61468d74c113",{"id":1466,"createTime":20,"updateTime":20,"relativeEntities":1468,"slug":20,"properties":1469,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1472,"statistic":20},[],{"title":1470},{"VI":1471},"Department of Biomedical Engineering, College of Medical Convergence, Catholic Kwandong University, Gangneung, Republic of Korea",[],{"title":1474},{"VI":1475},"Ahnryul Choi",{"url":1442,"publisher":1477,"properties":1522},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1478,"slug":10,"properties":1479,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1482,"manageAffiliations":1491,"indexDatabases":1502,"url":20,"thumbnailPath":20,"statistic":1517,"gsStatistic":20,"type":115,"analyzePriority":20},[],{"issn":1480,"title":1481},{"VOID":13},{"EN":15},[1483,1487],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1484,"label":1485,"description":1486,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1488,"label":1489,"description":1490,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[1492,1497],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":1493,"slug":20,"properties":1494,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1496,"statistic":20},[],{"title":1495},{"EN":41},[],{"id":44,"createTime":20,"updateTime":20,"relativeEntities":1498,"slug":20,"properties":1499,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1501,"statistic":20},[],{"title":1500},{"EN":48},[],[1503,1510],{"id":52,"indexDatabase":1504,"url":65,"indexYears":20,"academicFieldIds":1509,"indexDatabaseRanking":20},{"id":54,"createTime":20,"updateTime":20,"relativeEntities":1505,"label":1506,"description":1507,"key":61,"publicationTags":1508,"standard":20},[],{"EN":57,"VI":57},{"EN":59,"VI":60},[63,64],[67,68,69],{"id":71,"indexDatabase":1511,"url":82,"indexYears":83,"academicFieldIds":1516,"indexDatabaseRanking":87},{"id":73,"createTime":20,"updateTime":20,"relativeEntities":1512,"label":1513,"description":1514,"key":79,"publicationTags":1515,"standard":20},[],{"EN":76,"VI":76},{"EN":76,"VI":78},[81],[85,86],{"impactFactor":21,"impactFactorByYear":1518,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":91,"totalPublicationByYear":1519,"totalCitation":110,"totalCitationByYear":1520,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1521,"hindexLast5Year":114,"hindex":114},{"2023":90},{"2004":93,"2005":94,"2006":95,"2007":96,"2008":95,"2009":97,"2010":96,"2011":97,"2012":98,"2013":99,"2014":100,"2015":101,"2016":102,"2017":103,"2018":101,"2019":104,"2020":105,"2021":106,"2022":107,"2023":108,"2024":109},{"2022":110},{"2022":113},{"pages":1523,"volume":1525},{"VOID":1524},"1-14",{"VOID":538},57,{"total":1526,"publishYear":540,"statisticByYear":1528},{"2022":110,"2023":652,"2024":94,"2025":94,"2026":1529},11,"2022-01-16","DONE_ANALYZE_CITATION","2026-07-25T23:01:18.207+00:00",[63,87],{"id":1535,"createTime":1536,"updateTime":1537,"relativeEntities":1538,"slug":1539,"properties":1540,"entityType":135,"verifyStatus":136,"verifyTime":1549,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1550,"fullTextUrl":20,"authors":1551,"publicationType":214,"publisherRelationship":1671,"citationCount":21,"citationInfo":1722,"publishDate":1725,"publishYear":1723,"citationAnalyzeStatus":19,"lastCitationAnalyze":1537,"indexDatabases":1726,"openAccess":20,"references":1727,"isForceReanalyzing":412},"a52f0a5f-b18f-4469-806a-987004dd397a","2024-02-21T06:31:28.682+00:00","2026-07-21T12:03:34.791+00:00",[],"Natural-interfaces-and-virtual-environments-for-the-acquisition-of-street-crossing-and-path-following-skills-in-adults-with-Autism-Spectrum-Disorders-a-feasibility-study",{"abstract":1541,"title":1543,"gsPaper":1545,"doi":1547},{"EN":1542},"Lack of social skills and\u002For a reduced ability to determine when to use them are common symptoms of Autism Spectrum Disorder (ASD). Here we examine whether an integrated approach based on virtual environments and natural interfaces is effective in teaching safety skills in adults with ASD. We specifically focus on pedestrian skills, namely street crossing with or without traffic lights, and following road signs. Seven adults with ASD explored a virtual environment (VE) representing a city (buildings, sidewalks, streets, squares), which was continuously displayed on a wide screen. A markerless motion capture device recorded the subjects’ movements, which were translated into control commands for the VE according to a predefined vocabulary of gestures. The treatment protocol consisted of ten 45-minutes sessions (1 session\u002Fweek). During a familiarization phase, the participants practiced the vocabulary of gestures. In a subsequent training phase, participants had to follow road signs (to either a police station or a pharmacy) and to cross streets with and without traffic lights. We assessed the performance in both street crossing (number and type of errors) and navigation (walking speed, path length and ability to turn without stopping). To assess their understanding of the practiced skill, before and after treatment subjects had to answer a test questionnaire. To assess transfer of the learned skill to real-life situations, another specific questionnaire was separately administered to both parents\u002Flegal guardians and the subjects’ personal caregivers. One subject did not complete the familiarization phase because of problems with depth perception. The six subjects who completed the protocol easily learned the simple body gestures required to interact with the VE. Over sessions they significantly improved their navigation performance, but did not significantly reduce the errors made in street crossing. In the test questionnaire they exhibited no significant reduction in the number of errors. However, both parents and caregivers reported a significant improvement in the subjects’ street crossing performance. Their answers were also highly consistent, thus pointing at a significant transfer to real-life behaviors. Rehabilitation of adults with ASD mainly focuses on educational interventions that have an impact in their quality of life, which includes safety skills. Our results confirm that interaction with VEs may be effective in facilitating the acquisition of these skills.",{"EN":1544},"Natural interfaces and virtual environments for the acquisition of street crossing and path following skills in adults with Autism Spectrum Disorders: a feasibility study",{"VOID":1546},"[\"11233075206513072780\"]",{"VOID":1548},"10.1186\u002Fs12984-015-0010-z","2024-05-04T13:43:56.023+00:00","https:\u002F\u002Fjneuroengrehab.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12984-015-0010-z",[1552,1576,1589,1604,1619,1632,1645,1658],{"id":1553,"sortIndex":21,"researcher":20,"roles":1554,"affiliations":1555,"properties":1573,"displayName":1575,"givenName":20,"familyName":20},"6b325d8d-81df-402b-a90f-92466134115e",[144],[1556,1564],{"id":1557,"sortIndex":21,"affiliation":1558,"properties":20},"cac44a78-f8ab-4f48-9461-0e3f91bd604e",{"id":1557,"createTime":20,"updateTime":20,"relativeEntities":1559,"slug":20,"properties":1560,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1563,"statistic":20},[],{"title":1561},{"VI":1562},"Department Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy",[],{"id":1565,"sortIndex":114,"affiliation":1566,"properties":1572},"9f356c9e-9f04-43a6-896b-99826ff74330",{"id":1565,"createTime":20,"updateTime":20,"relativeEntities":1567,"slug":20,"properties":1568,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1571,"statistic":20},[],{"title":1569},{"VI":1570},"Department Primary Care, ASL3 Genovese, Genoa, Italy",[],{},{"title":1574},{"VI":1575},"Mario Saiano",{"id":1577,"sortIndex":114,"researcher":20,"roles":1578,"affiliations":1579,"properties":1586,"displayName":1588,"givenName":20,"familyName":20},"3a70f45a-1f23-4671-94dc-38af53a340e1",[144],[1580],{"id":1557,"sortIndex":21,"affiliation":1581,"properties":20},{"id":1557,"createTime":20,"updateTime":20,"relativeEntities":1582,"slug":20,"properties":1583,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1585,"statistic":20},[],{"title":1584},{"VI":1562},[],{"title":1587},{"VI":1588},"Laura Pellegrino",{"id":1590,"sortIndex":174,"researcher":20,"roles":1591,"affiliations":1592,"properties":1599,"displayName":1601,"givenName":20,"familyName":20},"196ca476-008c-4ac5-893f-f96b65e786d3",[144],[1593],{"id":1557,"sortIndex":21,"affiliation":1594,"properties":20},{"id":1557,"createTime":20,"updateTime":20,"relativeEntities":1595,"slug":20,"properties":1596,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1598,"statistic":20},[],{"title":1597},{"VI":1562},[],{"title":1600,"gsAuthor":1602},{"VI":1601},"Maura 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Crossing roads safely: An experimental study of age differences in gap selection by pedestrians. Accident Anal Prev. 2005;37:962–71.",{"doi":287},{"id":283,"text":1732,"url":285,"identifiers":1733},"Wright T, Wolery M. The effects of instructional interventions related to street crossing and individuals with disabilities. Res Dev Disabil. 2011;32:1455–63.",{"doi":287},{"id":20,"text":1735,"url":20,"identifiers":1736},"American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders - DSM-5. 5th edn: Amer Psychiatric Pub; 2013.",{},{"id":283,"text":1738,"url":285,"identifiers":1739},"Pennington BF, Ozonoff S. Executive functions and developmental psychopathology. J Child Psychol Psychiatry. 1996;37:51–87.",{"doi":287},{"id":283,"text":1741,"url":285,"identifiers":1742},"Billstedt E, Gillberg IC, Gillberg C. Autism after adolescence: population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. 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Computer graphics applications in the education process of people with learning difficulties. Comput Graph-Uk. 2007;31:649–58.",{"doi":287},{"id":283,"text":1771,"url":285,"identifiers":1772},"Strickland D, Marcus LM, Mesibov GB, Hogan K. Brief report: Two case studies using virtual reality as a learning tool for autistic children. J Autism Dev Disord. 1996;26:651–9.",{"doi":287},{"id":283,"text":1774,"url":285,"identifiers":1775},"Parsons S, Mitchell P, Leonard A. The use and understanding of virtual environments by adolescents with autistic spectrum disorders. J Autism Dev Disord. 2004;34:449–66.",{"doi":287},{"id":283,"text":1777,"url":285,"identifiers":1778},"Mitchell P, Parsons S, Leonard A. Using virtual environments for teaching social understanding to 6 adolescents with autistic spectrum disorders. J Autism Dev Disord. 2007;37:589–600.",{"doi":287},{"id":20,"text":1780,"url":20,"identifiers":1781},"Herrera G, Alcantud F, Jordan R, Blanquer A, Labajo G, De Pablo C. Development of symbolic play through the use of virtual reality tools in children with autistic spectrum disorders: two case studies. Autism. 2008;12:143–57.",{},{"id":283,"text":1783,"url":285,"identifiers":1784},"Yufang C, Moore D, McGrath P, Yulei F. Collaborative virtual environment technology for people with autism. In Fifth IEEE International Conference on Advanced Learning Technologies (ICALT 2005); 5–8 July 2005. 2005: 247–248.",{"doi":287},{"id":1786,"text":1787,"url":1788,"identifiers":1789},"a20cf6cc-3f3e-4a2e-beff-bf4e8d0e5beb","Cheng YF, Ye J. Exploring the social competence of students with autism spectrum conditions in a collaborative virtual learning environment - The pilot study. Comput Educ. 2010;54:1068–77.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0360131509002966",{"doi":1790},"10.1016\u002Fj.compedu.2009.10.011",{"id":1792,"text":1793,"url":1794,"identifiers":1795},"648b34bb-064a-489c-a865-eac66c3f9bcf","Mengue-Topio H, Courbois Y, Farran EK, Sockeel P. Route learning and shortcut performance in adults with intellectual disability: a study with virtual environments. Res Dev Disabil. 2011;32:345–52.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0891422210002520",{"doi":1796},"10.1016\u002Fj.ridd.2010.10.014",{"id":20,"text":1798,"url":20,"identifiers":1799},"Goldsmith TR: Using Virtual Reality Enhanced Behavioral Skills Training to Teach Street-crossing Skills to Children and Adolescents with Autism Spectrum Disorders. Western Michigan University, 2008",{},{"id":283,"text":1801,"url":285,"identifiers":1802},"Josman N, Ben-Chaim HM, Friedrich S, Weiss PL. Effectiveness of virtual reality for teaching street-crossing skills to children and adolescents with autism. Int J Disabil Hum Dev. 2008;7:49–56.",{"doi":287},{"id":283,"text":1804,"url":285,"identifiers":1805},"Katz N, Ring H, Naveh Y, Kizony R, Feintuch U, Weiss PL. Interactive virtual environment training for safe street crossing of right hemisphere stroke patients with unilateral spatial neglect. Disabil Rehabil. 2005;27:1235–43.",{"doi":287},{"id":283,"text":1807,"url":285,"identifiers":1808},"Schwebel DC, Gaines J, Severson J. Validation of virtual reality as a tool to understand and prevent child pedestrian injury. Accident Anal Prev. 2008;40:1394–400.",{"doi":287},{"id":1810,"text":1811,"url":1812,"identifiers":1813},"db99e47c-13d4-4f20-a382-891f92ce3c7a","Schwebel DC, McClure LA, Severson J. Usability and feasibility of an internet-based virtual pedestrian environment to teach children to cross streets safely. 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Neuroepidemiology. 2011;36:2–18.",{"doi":287},{"id":283,"text":1828,"url":285,"identifiers":1829},"Agarwal R, Sampath HA, Indurkhya B. A Usability Study on Natural Interaction Devices with ASD Children. In Universal Access in Human-Computer Interaction User and Context Diversity. Volume 8010. Edited by Stephanidis C, Antona M: Springer Berlin Heidelberg; 2013: 447–453: Lecture Notes in Computer Science].",{"doi":287},{"id":20,"text":1831,"url":20,"identifiers":1832},"Casas X, Herrera G, Coma I, Fernández M. A Kinect-based Augmented Reality System for Individuals with Autism Spectrum Disorders. In: Book A Kinect-based Augmented Reality System for Individuals with Autism Spectrum Disorders. City: SciTePress; 2012. p. 440–6.",{},{"id":20,"text":1834,"url":20,"identifiers":1835},"Wechsler D. WAIS-R: Wechsler Adult Intelligence Scale. Revised: Psychological Corporation; 1981.",{},{"id":283,"text":1837,"url":285,"identifiers":1838},"Orsini A, Laicardi C. Factor structure of the Italian version of the WAIS-R compared with the American standardization. Percept Motor Skill. 2000;90:1091–100.",{"doi":287},{"id":283,"text":1840,"url":285,"identifiers":1841},"Klin A, Volkmar FR, Sparrow SS, Cicchetti DV, Rourke BP. Validity and Neuropsychological Characterization of Asperger Syndrome - Convergence with Nonverbal Learning-Disabilities Syndrome. J Child Psychol Psyc. 1995;36:1127–40.",{"doi":287},{"id":283,"text":1843,"url":285,"identifiers":1844},"Riva G, Mantovani F, Capideville CS, Preziosa A, Morganti F, Villani D, et al. Affective interactions using virtual reality: the link between presence and emotions. Cyberpsychol Behavior. 2007;10:45–56.",{"doi":287},{"id":283,"text":1846,"url":285,"identifiers":1847},"Dutta T. Evaluation of the Kinect (TM) sensor for 3-D kinematic measurement in the workplace. Appl Ergon. 2012;43:645–9.",{"doi":287},{"id":283,"text":1849,"url":285,"identifiers":1850},"Suma EA, Lange B, Rizzo A, Krum DM, Bolas M: FAAST: The Flexible Action and Articulated Skeleton Toolkit. In Book FAAST: The Flexible Action and Articulated Skeleton Toolkit (Editor ed.^eds.). pp. 247–248. City; 2011:247–248.",{"doi":287},{"id":283,"text":1852,"url":285,"identifiers":1853},"Conditt MA, Gandolfo F, Mussa-Ivaldi FA. The motor system does not learn the dynamics of the arm by rote memorization of past experience. J Neurophysiol. 1997;78:554–60.",{"doi":287},{"id":283,"text":1855,"url":285,"identifiers":1856},"Tanaka N, Takagi H. Virtual reality environment design of managing both presence and virtual reality sickness. J Physiol Anthropol Appl Human Sci. 2004;23:313–7.",{"doi":287},{"id":283,"text":1858,"url":285,"identifiers":1859},"Kaplan M. Seeing Through New Eyes: Changing the Lives of Autistic Children, Asperger Syndrome and Other Developmental Disabilities Through Vision Therapy. London, UK: Jessica Kingsley Publishers; 2006.",{"doi":287},{"id":1861,"text":1862,"url":1863,"identifiers":1864},"c36e559c-6f0e-4cb2-8001-1c48cefdbb49","Simmons DR, Robertson AE, McKay LS, Toal E, McAleer P, Pollick FE. Vision in autism spectrum disorders. Vision Res. 2009;49:2705–39.","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0042698909003563",{"doi":1865},"10.1016\u002Fj.visres.2009.08.005",{"id":283,"text":1867,"url":285,"identifiers":1868},"Max ML, Burke JC. Virtual reality for autism communication and education, with lessons for medical training simulators. Stud Health Technol Inform. 1997;39:46–53.",{"doi":287},{"id":20,"text":1870,"url":20,"identifiers":1871},"Eynon A. Computer interaction: An update on the AVATAR program. Communication. 1997. Summer",{},{"id":283,"text":1873,"url":285,"identifiers":1874},"Hilton CL, Cumpata K, Klohr C, Gaetke S, Artner A, Johnson H, et al. Effects of exergaming on executive function and motor skills in children with autism spectrum disorder: a pilot study. Am J Occup Ther. 2014;68:57–65.",{"doi":287},{"id":20,"text":1876,"url":20,"identifiers":1877},"Myers SM, Johnson CP, American Academy of Pediatrics Council on Children With Disabilities. Management of children with autism spectrum disorders. Pediatrics. 2007;120:1162–82.",{},{"id":1879,"createTime":1880,"updateTime":1881,"relativeEntities":1882,"slug":1883,"properties":1884,"entityType":135,"verifyStatus":136,"verifyTime":1893,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1894,"fullTextUrl":20,"authors":1895,"publicationType":214,"publisherRelationship":1956,"citationCount":21,"citationInfo":2006,"publishDate":2009,"publishYear":2007,"citationAnalyzeStatus":1531,"lastCitationAnalyze":2010,"indexDatabases":2011,"openAccess":20,"references":2012,"isForceReanalyzing":412},"186d4415-2b7f-400a-809e-b949a8b6be42","2024-01-13T01:20:59.249+00:00","2026-07-19T06:16:28.422+00:00",[],"Time-and-frequency-domain-methods-for-quantifying-common-modulation-of-motor-unit-firing-patterns",{"abstract":1885,"title":1887,"gsPaper":1889,"doi":1891},{"EN":1886},"In investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'. The relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence. Analytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r2 regression statistics (p \u003C 0.05) are 0.56, 0.81 and 0.80 respectively. Although theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited.",{"EN":1888},"Time and frequency domain methods for quantifying common modulation of motor unit firing 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P: Cortical drives to human muscle: the Piper and related rhythms. 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Neuroreport 2001, 12: 2577-2581. 10.1097\u002F00001756-200108080-00057","http:\u002F\u002Fjournals.lww.com\u002F00001756-200108080-00057",{"doi":2068},"10.1097\u002F00001756-200108080-00057",{"id":2070,"text":2071,"url":2072,"identifiers":2073},"8a68af59-7749-4693-99be-7e48605a8fdf","Salenius S, Avikainen S, Kaakkola S, Hari R, Brown P: Defective cortical drive to muscle in Parkinson's disease and its improvement with levodopa. Brain 2002, 125: 491-500. 10.1093\u002Fbrain\u002Fawf042","https:\u002F\u002Facademic.oup.com\u002Fbrain\u002Farticle-lookup\u002Fdoi\u002F10.1093\u002Fbrain\u002Fawf042",{"doi":2074},"10.1093\u002Fbrain\u002Fawf042",{"id":2076,"text":2077,"url":2078,"identifiers":2079},"c4878747-cb7c-4aa5-a2cf-4553e0bfc9be","Farmer SF, Sheean GL, Mayston MJ, Rothwell JC, Marsden CD, Conway BA, Halliday DM, Rosenberg JR, Stephens JA: Abnormal motor unit synchronization of antagonist muscles underlies pathological co-contraction in upper limb dystonia. Brain 1998, 121: 801-814. 10.1093\u002Fbrain\u002F121.5.801","https:\u002F\u002Facademic.oup.com\u002Fbrain\u002Farticle-lookup\u002Fdoi\u002F10.1093\u002Fbrain\u002F121.5.801",{"doi":2080},"10.1093\u002Fbrain\u002F121.5.801",{"id":283,"text":2082,"url":285,"identifiers":2083},"Mima T, Toma K, Koshy B, Hallett M: Coherence between cortical and muscular activities after subcortical stroke. Stroke 2001, 32: 2597-2601.",{"doi":287},{"id":2085,"text":2086,"url":2087,"identifiers":2088},"35284074-26e6-4bc9-9201-83d01a03522f","Grosse P, Guerrini R, Parmeggiani L, Bonanni P, Pogosyan A, Brown P: Abnormal corticomuscular and intermuscular coupling in high-frequency rhythmic myoclonus. Brain 2003, 126: 326-342. 10.1093\u002Fbrain\u002Fawg043","https:\u002F\u002Facademic.oup.com\u002Fbrain\u002Farticle-lookup\u002Fdoi\u002F10.1093\u002Fbrain\u002Fawg043",{"doi":2089},"10.1093\u002Fbrain\u002Fawg043",{"id":283,"text":2091,"url":285,"identifiers":2092},"De Luca CJ, LeFever RS, McCue MP, Xenakis AP: Control scheme governing concurrently active human motor units during voluntary contractions. J Physiol 1982, 329: 129-142.",{"doi":287},{"id":2094,"text":2095,"url":2096,"identifiers":2097},"63f5a036-7b57-41df-920a-95320a381b79","Kamen G, Greenstein SS, De Luca CJ: Lateral dominance and motor unit firing behavior. Brain Res 1992, 576: 165-167. 10.1016\u002F0006-8993(92)90625-J","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F000689939290625J",{"doi":2098},"10.1016\u002F0006-8993(92)90625-j",{"id":2100,"text":2101,"url":2102,"identifiers":2103},"cdacff80-0316-4971-9866-454fb623e39f","Semmler JG, Nordstrom MA: Influence of handedness on motor unit discharge properties and force tremor. Exp RBraines 1995, 104: 115-125.","http:\u002F\u002Flink.springer.com\u002F10.1007\u002FBF00229861",{"doi":2104},"10.1007\u002Fbf00229861",{"id":283,"text":2106,"url":285,"identifiers":2107},"Adam A, De Luca CJ, Erim Z: Hand dominance and motor unit firing behavior. J Neurophysiol 1998, 80: 1373-1382.",{"doi":287},{"id":2109,"text":2110,"url":2111,"identifiers":2112},"b3de6918-2cbb-43c1-ab91-9456552ea53d","Garland SJ, Miles TS: Control of motor units in human ffexor digitorum profundus under different proprioceptive conditions. J Physiol 1997, 502: 693-701. 10.1111\u002Fj.1469-7793.1997.693bj.x","https:\u002F\u002Fphysoc.onlinelibrary.wiley.com\u002Fdoi\u002F10.1111\u002Fj.1469-7793.1997.693bj.x",{"doi":2113},"10.1111\u002Fj.1469-7793.1997.693bj.x",{"id":2115,"text":2116,"url":2117,"identifiers":2118},"356102d7-4054-4eec-ae35-6330b321bf93","Semmler JG, Nordstrom MA, Wallace CJ: Relationship between motor unit short-term synchronization and common drive in human first dorsal interosseous muscle. Brain Res 1997, 767: 314-320. 10.1016\u002FS0006-8993(97)00621-5","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0006899397006215",{"doi":2119},"10.1016\u002FS0006-8993(97)00621-5",{"id":2121,"text":2122,"url":2123,"identifiers":2124},"25f62eab-8c5a-4873-99aa-902f2199d913","Patten C, Kamen G: Adaptations in motor unit discharge activity with force control training in young and older human adults. Eur J Appl Physiol 2000, 83: 128-143. 10.1007\u002Fs004210000271","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs004210000271",{"doi":2125},"10.1007\u002Fs004210000271",{"id":283,"text":2127,"url":285,"identifiers":2128},"Erim Z, Beg MF, Burke DT, de Luca CJ: Effects of aging on motor-unit control properties. J Neurophysiol 1999, 82: 2081-2091.",{"doi":287},{"id":283,"text":2130,"url":285,"identifiers":2131},"Carter GC: Coherence and time delay estimation. Proc IEEE 1987, 75: 236-255.",{"doi":287},{"id":2133,"text":2134,"url":2135,"identifiers":2136},"1a315b90-1d2d-48e8-9e9e-1fde67dc8cb5","Gardner WA: A unifying view of coherence in signal processing. Signal Processing 1992, 29: 113-140. 10.1016\u002F0165-1684(92)90015-O","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F016516849290015O",{"doi":2137},"10.1016\u002F0165-1684(92)90015-o",{"id":2139,"text":2140,"url":2141,"identifiers":2142},"37f9ebb3-c918-4fa2-9471-61b9fd4ce346","De Luca CJ, Erim Z: Common drive of motor units in regulation of muscle force. Trends Neurosci 1994, 17: 299-305. 10.1016\u002F0166-2236(94)90064-7","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F0166223694900647",{"doi":2143},"10.1016\u002F0166-2236(94)90064-7",{"id":283,"text":2145,"url":285,"identifiers":2146},"Marple SL: Digital spectral analysis with applications. Englewood Cliffs, NJ: Prentice Hall; 1987.",{"doi":287},{"id":2148,"text":2149,"url":2150,"identifiers":2151},"0d35c26b-ec74-46eb-94db-934c3c29ecaa","Kristeva-Feige R, Fritsch C, Timmer J, Lucking CH: Effects of attention and precision of exerted force on beta range EEG-EMG synchronization during a maintained motor contraction task. Clin Neurophysiol 2002, 113: 124-131. 10.1016\u002FS1388-2457(01)00722-2","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1388245701007222",{"doi":2152},"10.1016\u002Fs1388-2457(01)00722-2",{"id":2154,"text":2155,"url":2156,"identifiers":2157},"d266f149-b79b-4d60-934c-dc261517f9b7","Gross J, Tass PA, Salenius S, Hari R, Freund HJ, Schnitzler A: Cortico-muscular synchronization during isometric muscle contraction in humans as revealed by magnetoencephalography. J Physiol 2000, 527: 623-631. 10.1111\u002Fj.1469-7793.2000.00623.x","https:\u002F\u002Fphysoc.onlinelibrary.wiley.com\u002Fdoi\u002F10.1111\u002Fj.1469-7793.2000.00623.x",{"doi":2158},"10.1111\u002Fj.1469-7793.2000.00623.x",{"id":2160,"text":2161,"url":2162,"identifiers":2163},"b9dd9629-6c26-4330-8e58-09fd98eb4b15","Amjad AM, Halliday DM, Rosenberg JR, Conway BA: An extended difference of coherence test for comparing and combining several independent coherence estimates: theory and application to the study of motor units and physiological tremor. J Neurosci Methods 1997, 73: 69-79. 10.1016\u002FS0165-0270(96)02214-5","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0165027096022145",{"doi":2164},"10.1016\u002Fs0165-0270(96)02214-5",{"id":2166,"text":2167,"url":2168,"identifiers":2169},"690037e7-f8bd-4c40-877f-78f001a06089","Cassidy MJ, Brown P: Hidden Markov based autoregressive analysis of stationary and non-stationary electrophysiological signals for functional coupling studies. J Neurosci Methods 2002, 116: 35-53. 10.1016\u002FS0165-0270(02)00026-2","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0165027002000262",{"doi":2170},"10.1016\u002Fs0165-0270(02)00026-2",{"id":2172,"text":2173,"url":2174,"identifiers":2175},"d123bc8b-b0b4-4a43-9cf7-c915ca4fd081","Mima T, Hallett M: Electroencephalographic analysis of cortico-muscular coherence: reference effect, volume conduction and generator mechanism. Clin Neurophysiol 1999, 110: 1892-1899. 10.1016\u002FS1388-2457(99)00238-2","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS1388245799002382",{"doi":2176},"10.1016\u002Fs1388-2457(99)00238-2",{"id":2178,"createTime":2179,"updateTime":2180,"relativeEntities":2181,"slug":2182,"properties":2183,"entityType":135,"verifyStatus":136,"verifyTime":2192,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":2193,"fullTextUrl":20,"authors":2194,"publicationType":214,"publisherRelationship":2235,"citationCount":2285,"citationInfo":2286,"publishDate":2292,"publishYear":540,"citationAnalyzeStatus":19,"lastCitationAnalyze":2293,"indexDatabases":2294,"openAccess":20,"references":2295,"isForceReanalyzing":412},"c08775af-4531-4ebe-806e-38db7c409f3f","2024-01-08T14:16:42.340+00:00","2026-07-16T17:58:41.907+00:00",[],"Non-invasive-brain-stimulation-for-improving-gait-balance-and-lower-limbs-motor-function-in-stroke",{"abstract":2184,"title":2186,"gsPaper":2188,"doi":2190},{"EN":2185},"This systematic review and meta-analysis aim to summarize and analyze the available evidence of non-invasive brain stimulation\u002Fspinal cord stimulation on gait, balance and\u002For lower limb motor recovery in stroke patients. The PubMed database was searched from its inception through to 31\u002F03\u002F2021 for randomized controlled trials investigating repetitive transcranial magnetic stimulation or transcranial\u002Ftrans-spinal direct current\u002Falternating current stimulation for improving gait, balance and\u002For lower limb motor function in stroke patients. Overall, 25 appropriate studies (including 657 stroke subjects) were found. The data indicates that non-invasive brain stimulation\u002Fspinal cord stimulation is effective in supporting recovery. However, the effects are inhomogeneous across studies: (1) transcranial\u002Ftrans-spinal direct current\u002Falternating current stimulation induce greater effects than repetitive transcranial magnetic stimulation, and (2) bilateral application of non-invasive brain stimulation is superior to unilateral stimulation. The current evidence encourages further research and suggests that more individualized approaches are necessary for increasing effect sizes in stroke patients.",{"EN":2187},"Non-invasive brain stimulation for improving gait, balance, and lower limbs motor function in 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Priming with 1-Hz repetitive transcranial magnetic stimulation over contralesional leg motor cortex does not increase the rate of regaining ambulation within 3 months of stroke: a randomized controlled trial. Am J Phys Med Rehabil. 2018;97:339–45.",{},{"id":283,"text":2524,"url":285,"identifiers":2525},"Kim WS, Jung SH, Oh MK, et al. Effect of repetitive transcranial magnetic stimulation over the cerebellum on patients with ataxia after posterior circulation stroke: a pilot study. J Rehabil Med. 2014;46:418–23.",{"doi":287},{"id":20,"text":2527,"url":20,"identifiers":2528},"Koch G, Bonnì S, Casula EP, et al. Effect of cerebellar stimulation on gait and balance recovery in patients with hemiparetic stroke: a randomized clinical trial. JAMA Neurol. 2019;76:170–8.",{},{"id":283,"text":2530,"url":285,"identifiers":2531},"Lin LF, Chang KH, Huang YZ, et al. Simultaneous stimulation in bilateral leg motor areas with intermittent theta burst stimulation to improve functional performance after stroke: a feasibility pilot study. Eur J Phys Rehabil Med. 2019;55:162–8.",{"doi":287},{"id":283,"text":2533,"url":285,"identifiers":2534},"Rastgoo M, Naghdi S, Nakhostin Ansari N, et al. Effects of repetitive transcranial magnetic stimulation on lower extremity spasticity and motor function in stroke patients. Disabil Rehabil. 2016;38:1918–26.",{"doi":287},{"id":2536,"text":2537,"url":2538,"identifiers":2539},"12f79aa2-ea86-4b35-a1e7-40033f94bfcd","Sasaki N, Abo M, Hara T, et al. High-frequency rTMS on leg motor area in the early phase of stroke. Acta Neurol Belg. 2017;117:189–94.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13760-016-0687-1",{"doi":2540},"10.1007\u002Fs13760-016-0687-1",{"id":2542,"text":2543,"url":2544,"identifiers":2545},"ddc6ddf6-1a11-49ba-b67e-93030481dbc0","Wang RY, Wang FY, Huang SF, Yang YR. High-frequency repetitive transcranial magnetic stimulation enhanced treadmill training effects on gait performance in individuals with chronic stroke: a double-blinded randomized controlled pilot trial. Gait Posture. 2019;68:382–7.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS096663621831347X",{"doi":2546},"10.1016\u002Fj.gaitpost.2018.12.023",{"id":283,"text":2548,"url":285,"identifiers":2549},"Fleming MK, Pavlou M, Newham DJ, et al. Non-invasive brain stimulation for the lower limb after stroke: what do we know so far and what should we be doing next? Disabil Rehabil. 2017;39:714–20.",{"doi":287},{"id":283,"text":2551,"url":285,"identifiers":2552},"Kang N, Lee RD, Lee JH, Hwang MH. Functional balance and postural control improvements in patients with stroke after noninvasive brain stimulation: a meta-analysis. Arch Phys Med Rehabil. 2020;101:141–53.",{"doi":287},{"id":283,"text":2554,"url":285,"identifiers":2555},"Tien HH, Liu WY, Chen YL, Chen YL, Wu YC, Lien HY. Transcranial direct current stimulation for improving ambulation after stroke: a systematic review and meta-analysis. Int J Rehabil Res. 2020;43:299–309.",{"doi":287},{"id":283,"text":2557,"url":285,"identifiers":2558},"Tung YC, Lai CH, Liao CD, Huang SW, Liou TH, Chen HC. Repetitive transcranial magnetic stimulation of lower limb motor function in patients with stroke: a systematic review and meta-analysis of randomized controlled trials. Clin Rehabil. 2019;33:1102–12.",{"doi":287},{"id":283,"text":2560,"url":285,"identifiers":2561},"Sawaki L, Butler AJ, Leng X, Wassenaar PA, Mohammad YM, Blanton S, et al. Differential patterns of cortical reorganization following constraint-induced movement therapy during early and late period after stroke: a preliminary study. 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Brain Stimul. 2020; 13: 1476–1488.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1935861X20302163",{"doi":2573},"10.1016\u002Fj.brs.2020.07.018",{"id":20,"text":2575,"url":20,"identifiers":2576},"Corp DT, Bereznicki HGK, Clark GM, Youssef GJ, Fried PJ, Jannati A, et al. 'Big TMS Data Collaboration'. Large-scale analysis of interindividual variability in single and paired-pulse TMS data. Clin Neurophysiol. 2021; 132: 2639–2653.",{},{"id":283,"text":2578,"url":285,"identifiers":2579},"Ridding MC, Ziemann U. Determinants of the induction of cortical plasticity by non-invasive brain stimulation in healthy subjects. J Physiol. 2010;588:2291–304.",{"doi":287},{"id":283,"text":2581,"url":285,"identifiers":2582},"Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T, et al. Safety of transcranial direct current stimulation: evidence based update 2016. Brain Stimul. 2016;9:641–61.",{"doi":287},{"id":283,"text":2584,"url":285,"identifiers":2585},"Khadka N, Borges H, Paneri B, Kaufman T, Nassis E, Zannou AL, et al. Adaptive current tDCS up to 4 mA. Brain Stimul. 2020;13:69–79.",{"doi":287},{"id":2587,"text":2588,"url":2589,"identifiers":2590},"e14ed865-cd85-4958-9235-5e2036e2b5c8","Sallard E, Rohrbach JL Brandner C, Place N, Barral J. Individualization of tDCS intensity according to corticospinal excitability does not improve stimulation efficacy over the primary motor cortex. Neuroimage Rep. 2021; 100028: 1–6.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS266695602100026X",{"doi":2591},"10.1016\u002Fj.ynirp.2021.100028",{"id":2593,"text":2594,"url":2595,"identifiers":2596},"c1dc0f45-4ace-45ce-b37b-b9750dfe975e","Rossi S, Antal A, Bestmann S, Bikson M, Brewer C, Brockmöller J, et al. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: expert guidelines. Clin Neurophysiol. 2021;132:269–306.","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS1388245720305149",{"doi":2597},"10.1016\u002Fj.clinph.2020.10.003",{"id":2599,"text":2600,"url":2601,"identifiers":2602},"e6a992b6-461a-4b89-9be8-f4e66b4b0800","Benninger DH, Lomarev M, Wassermann EM, Lopez G, Houdayer E, Fasano RE, et al. Safety study of 50 Hz repetitive transcranial magnetic stimulation in patients with Parkinson’s disease. Clin Neurophysiol. 2009;120:809–15.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1388245709000418",{"doi":2603},"10.1016\u002Fj.clinph.2009.01.012",{"id":283,"text":2605,"url":285,"identifiers":2606},"Torrecillos F, Falato E, Pogosyan A, West T, Di Lazzaro V, Brown P. 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EEG-guided transcranial magnetic stimulation reveals rapid shifts in motor cortical excitability during the human sleep slow oscillation. J Neurosci. 2012;32:243–53.",{"doi":287},{"id":283,"text":2620,"url":285,"identifiers":2621},"Zrenner C, Desideri D, Belardinelli P, Ziemann U. Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex. Brain Stimul. 2018;11:374–89.",{"doi":287},{"id":2623,"text":2624,"url":2625,"identifiers":2626},"8a6a4d2a-0591-4c40-8782-c4c84535d0eb","Madsen KH, Karabanov AN, Krohne LG, Safeldt MG, Tomasevic L, Siebner HR. No trace of phase: corticomotor excitability is not tuned by phase of pericentral mu-rhythm. Brain Stimul. 2019;12:1261–70.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1935861X19302128",{"doi":2627},"10.1016\u002Fj.brs.2019.05.005",{"id":283,"text":2629,"url":285,"identifiers":2630},"Gharabaghi A, Kraus D, Leão MT, Spüler M, Walter A, Bogdan M, et al. 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Cerebellum. 2020;19:131–53.",{"doi":287},{"id":2641,"text":2642,"url":2643,"identifiers":2644},"33eb4775-7ae7-4b14-9a12-f583fa3492f9","Deng ZD, Lisanby SH, Peterchev AV. Coil design considerations for deep transcranial magnetic stimulation. Clin Neurophysiol. 2014;125:1202–12.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1388245713013539",{"doi":2645},"10.1016\u002Fj.clinph.2013.11.038",{"id":283,"text":2647,"url":285,"identifiers":2648},"Rastogi P, Lee EG, Hadimani RL, Jiles DC. Transcranial magnetic stimulation: development of a novel deep brain coil—triple halo coil. IEEE Magn Lett. 2019;10:1–5.",{"doi":287},{"id":283,"text":2650,"url":285,"identifiers":2651},"Ameli M, Grefkes C, Kemper F, Riegg FP, Rehme AK, Karbe H, et al. Differential effects of high-frequency repetitive transcranial magnetic stimulation over ipsilesional primary motor cortex in cortical and subcortical middle cerebral artery stroke. Ann Neurol. 2009;66:298–309.",{"doi":287},{"id":283,"text":2653,"url":285,"identifiers":2654},"Lüdemann-Podubecká J, Bösl K, Theilig S, Wiederer R, Nowak DA. The effectiveness of 1 Hz rTMS over the primary motor area of the unaffected hemisphere to improve hand function after stroke depends on hemispheric dominance. Brain Stimul. 2015;8:823–30.",{"doi":287},{"id":283,"text":2656,"url":285,"identifiers":2657},"Luft AR, Forrester L, Macko RF, McCombe-Waller S, Whitall J, Villagra F, Hanley DF. Brain activation of lower extremity movement in chronically impaired stroke survivors. Neuroimage. 2005;26:184–94.",{"doi":287},{"id":2659,"createTime":2660,"updateTime":2661,"relativeEntities":2662,"slug":2663,"properties":2664,"entityType":135,"verifyStatus":136,"verifyTime":2675,"verifyNote":138,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":2676,"fullTextUrl":20,"authors":2677,"publicationType":214,"publisherRelationship":2860,"citationCount":20,"citationInfo":20,"publishDate":2910,"publishYear":2911,"citationAnalyzeStatus":19,"lastCitationAnalyze":2661,"indexDatabases":2912,"openAccess":20,"references":20,"isForceReanalyzing":412},"7493d428-150f-472a-8147-3c003738c44b","2024-01-01T08:59:14.983+00:00","2026-07-16T15:04:59.934+00:00",[],"Reliability-validity-and-discriminant-ability-of-the-instrumental-indices-provided-by-a-novel-planar-robotic-device-for-upper-limb-rehabilitation",{"abstract":2665,"title":2667,"gsPaper":2669,"references":2671,"doi":2673},{"EN":2666},"In the last few years, there has been an increasing interest in the use of robotic devices to objectively quantify motor performance of patients after brain damage. Although these robot-derived measures can potentially add meaningful information about the patient’s dexterity, as well as be used as outcome measurements after the rehabilitation treatment, they need to be validated before being used in clinical practice. The present work aims to evaluate the reliability, the validity and the discriminant ability of the metrics provided by a novel robotic device for upper limb rehabilitation. Forty-eight patients with sub-acute stroke and 40 age-matched healthy subjects were involved in this study. Clinical evaluation included: Fugl-Meyer Assessment for the upper limb, Action Research Arm Test, and Barthel Index. Robotic evaluation of the upper limb performance consisted of 14 measures of motor ability quantifying the dexterity in performing planar reaching movements. Patients were evaluated twice, one day apart, to assess the reliability of the robotic metrics, using the Intraclass Correlation Coefficient. Validity was assessed by analyzing the correlation of the robotic metrics with the clinical scales, by means of the Spearman’s Correlation Coefficient. Finally, the ability of the robotic metrics to distinguish between patients with stroke and healthy subjects was investigated with t-tests and the Effect Size. Reliability was found to be excellent for 12 measures and from moderate to good for the remaining 2. Most of the robotic indices were strongly correlated with the clinical scales, while a few showed a moderate correlation and only one was not correlated with the Barthel Index and weakly correlated with the remain two. Finally, all but one the provided metrics were able to discriminate between the two groups, with large effect sizes for most of them. We found that all the robotic indices except one provided by a novel robotic device for upper limb rehabilitation are reliable, sensitive and strongly correlated both with motor and disability clinical scales. Therefore, this device is suitable as evaluation tool for the upper limb motor performance of patients with sub-acute stroke in clinical practice. \n                    NCT02879279\n                    \n                  .",{"EN":2668},"Reliability, validity and discriminant ability of the instrumental indices provided by a novel planar robotic device for upper limb rehabilitation",{"VOID":2670},"[\"12926664787047343646\"]",{"VOID":2672},"Loureiro RCV, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput. 2011;49:1103–18.\nVolpe BT, Krebs HI, Hogan N. Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Curr Opin Neurol. 2001;14:745–52.\nSivan M, O’Connor RJ, Makower S, Levesley M, Bhakta B. Systematic review of outcome measures used in the evaluation of robot-assisted upper limb exercise in stroke. J Rehabil Med. 2011;43:181–9.\nCanning CG, Ada L, O’Dwyer NJ. 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Understanding correlation: factors that affect the size of r. J Exp Educ. 2006;74:249–66.\nKamper DG, McKenna-Cole AN, Kahn LE, Reinkensmeyer DJ. Alterations in reaching after stroke and their relation to movement direction and impairment severity. Arch Phys Med Rehabil. 2002;83:702–7.",{"VOID":2674},"10.1186\u002Fs12984-018-0385-8","2024-06-26T10:10:43.998+00:00","https:\u002F\u002Fjneuroengrehab.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12984-018-0385-8",[2678,2695,2708,2721,2743,2756,2769,2782,2795,2811,2824,2847],{"id":2679,"sortIndex":21,"researcher":20,"roles":2680,"affiliations":2681,"properties":2690,"displayName":2692,"givenName":20,"familyName":20},"742f5438-0179-4ddf-8eb2-98c326b46ae2",[144],[2682],{"id":2683,"sortIndex":21,"affiliation":2684,"properties":20},"01b6d888-104d-42fc-95d7-7cdd0651d96b",{"id":2683,"createTime":20,"updateTime":20,"relativeEntities":2685,"slug":20,"properties":2686,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":2689,"statistic":20},[],{"title":2687},{"VI":2688},"IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy",[],{"title":2691,"gsAuthor":2693},{"VI":2692},"Marco 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increased joint resistance into its contributing factors i.e. stiffness and viscosity (\"hypertonia\") and stretch reflexes (\"hyperreflexia\") is important in stroke rehabilitation. Existing clinical tests, such as the Ashworth Score, do not permit discrimination between underlying tissue and reflexive (neural) properties. We propose an instrumented identification paradigm for early and tailor made interventions. Ramp-and-Hold ankle dorsiflexion rotations of various durations were imposed using a manipulator. A one second rotation over the Range of Motion similar to the Ashworth condition was included. Tissue stiffness and viscosity and reflexive torque were estimated using a nonlinear model and compared to the Ashworth Score of nineteen stroke patients and seven controls. Ankle viscosity moderately increased, stiffness was indifferent and reflexive torque decreased with movement duration. Compared to controls, patients with an Ashworth Score of 1 and 2+ were significantly stiffer and had higher viscosity and patients with an Ashworth Score of 2+ showed higher reflexive torque. For the one second movement, stiffness correlated to Ashworth Score (r2 = 0.51, F = 32.7, p \u003C 0.001) with minor uncorrelated reflexive torque. Reflexive torque correlated to Ashworth Score at shorter movement durations (r2 = 0.25, F = 11, p = 0.002). Stroke patients were distinguished from controls by tissue stiffness and viscosity and to a lesser extent by reflexive torque from the soleus muscle. These parameters were also sensitive to discriminate patients, clinically graded by the Ashworth Score. Movement duration affected viscosity and reflexive torque which are clinically relevant parameters. Full evaluation of pathological joint resistance therefore requires instrumented tests at various movement conditions.",{"EN":2923},"The relation between neuromechanical parameters and Ashworth score in stroke patients",{"VOID":2925},"[\"15410856065718340244\"]",{"VOID":2927},"Katz RT, Rymer WZ: Spastic hypertonia: mechanisms and measurement. Arch Phys Med Rehabil 1989, 70: 144-155.\nDamiano DL, Quinlivan JM, Owen BF, Payne P, Nelson KC, Abel MF: What does the Ashworth scale really measure and are instrumented measures more valid and precise? Dev Med Child Neurol 2002, 44: 112-118. 10.1017\u002FS0012162201001761\nAshworth B: Preliminary Trial of Carisoprodol in Multiple Sclerosis. Practitioner 1964, 192: 540-542.\nPandyan AD, Johnson GR, Price CI, Curless RH, Barnes MP, Rodgers H: A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity. Clin Rehabil 1999, 13: 373-383. 10.1191\u002F026921599677595404\nPandyan AD, Price CI, Barnes MP, Johnson GR: A biomechanical investigation into the validity of the modified Ashworth Scale as a measure of elbow spasticity. Clin Rehabil 2003, 17: 290-293. 10.1191\u002F0269215503cr610oa\nStarsky AJ, Sangani SG, McGuire JR, Logan B, Schmit BD: Reliability of biomechanical spasticity measurements at the elbow of people poststroke. Arch Phys Med Rehabil 2005, 86: 1648-1654. 10.1016\u002Fj.apmr.2005.03.015\nChung SG, van Rey E, Bai Z, Rymer WZ, Roth EJ, Zhang LQ: Separate quantification of reflex and nonreflex components of spastic hypertonia in chronic hemiparesis. Arch Phys Med Rehabil 2008, 89: 700-710. 10.1016\u002Fj.apmr.2007.09.051\nAlibiglou L, Rymer WZ, Harvey RL, Mirbagheri MM: The relation between Ashworth scores and neuromechanical measurements of spasticity following stroke. J Neuroeng Rehabil 2008, 5: 18. 10.1186\u002F1743-0003-5-18\nMcCrea PH, Eng JJ, Hodgson AJ: Linear spring-damper model of the hypertonic elbow: reliability and validity. J Neurosci Methods 2003, 128: 121-128. 10.1016\u002FS0165-0270(03)00169-9\nde Vlugt E, Schouten AC, van der Helm FC: Adaptation of reflexive feedback during arm posture to different environments. Biol Cybern 2002, 87: 10-26. 10.1007\u002Fs00422-002-0311-8\nvan der Helm FC, Schouten AC, de Vlugt E, Brouwn GG: Identification of intrinsic and reflexive components of human arm dynamics during postural control. J Neurosci Methods 2002, 119: 1-14. 10.1016\u002FS0165-0270(02)00147-4\nHarlaar J, Becher JG, Snijders CJ, Lankhorst GJ: Passive stiffness characteristics of ankle plantar flexors in hemiplegia. Clin Biomech (Bristol, Avon) 2000, 15: 261-270. 10.1016\u002FS0268-0033(99)00069-8\nWeiss PL, Kearney RE, Hunter IW: Position dependence of ankle joint dynamics--I. Passive mechanics. J Biomech 1986, 19: 727-735. 10.1016\u002F0021-9290(86)90196-X\nMirbagheri MM, Barbeau H, Ladouceur M, Kearney RE: Intrinsic and reflex stiffness in normal and spastic, spinal cord injured subjects. Exp Brain Res 2001, 141: 446-459. 10.1007\u002Fs00221-001-0901-z\nGajdosik RLaL, DJ, McFarley DC, Meyer KM, Riggin TJ: Dynamic elastic and static viscoelastic stress-relaxation properties of the calf muscle-tendon unit of men and women. Isokinetics and Exercise Science 2006, 14: 33-44.\nLjung L: System Identification - Theory for the User. second edition. New Jersey: Prentice Hall; 1999.\nHouk JC, Rymer WZ, Crago PE: Dependence of dynamic response of spindle receptors on muscle length and velocity. J Neurophysiol 1981, 46: 143-166.\nBurne JA, Carleton VL, O'Dwyer NJ: The spasticity paradox: movement disorder or disorder of resting limbs? J Neurol Neurosurg Psychiatry 2005, 76: 47-54. 10.1136\u002Fjnnp.2003.034785\nLieber RL, Steinman S, Barash IA, Chambers H: Structural and functional changes in spastic skeletal muscle. Muscle Nerve 2004, 29: 615-627. 10.1002\u002Fmus.20059\nO'Dwyer NJ, Ada L, Neilson PD: Spasticity and muscle contracture following stroke. Brain 1996,119(Pt 5):1737-1749. 10.1093\u002Fbrain\u002F119.5.1737\nGajdosik RL: Influence of a low-level contractile response from the soleus, gastrocnemius and tibialis anterior muscles on viscoelastic stress-relaxation of aged human calf muscle-tendon units. Eur J Appl Physiol 2006, 96: 379-388. 10.1007\u002Fs00421-005-0091-7\nGajdosik RL, Vander Linden DW, McNair PJ, Riggin TJ, Albertson JS, Mattick DJ, Wegley JC: Viscoelastic properties of short calf muscle-tendon units of older women: effects of slow and fast passive dorsiflexion stretches in vivo. Eur J Appl Physiol 2005, 95: 131-139. 10.1007\u002Fs00421-005-1394-4\nSinger BJ, Dunne JW, Singer KP, Allison GT: Velocity dependent passive plantarflexor resistive torque in patients with acquired brain injury. Clin Biomech (Bristol, Avon) 2003, 18: 157-165. 10.1016\u002FS0268-0033(02)00173-0\nRabita G, Dupont L, Thevenon A, Lensel-Corbeil G, Perot C, Vanvelcenaher J: Differences in kinematic parameters and plantarflexor reflex responses between manual (Ashworth) and isokinetic mobilisations in spasticity assessment. Clin Neurophysiol 2005, 116: 93-100. 10.1016\u002Fj.clinph.2004.07.029\nMcNair PJ, Hewson DJ, Dombroski E, Stanley SN: Stiffness and passive peak force changes at the ankle joint: the effect of different joint angular velocities. Clin Biomech (Bristol, Avon) 2002, 17: 536-540. 10.1016\u002FS0268-0033(02)00062-1\nYeh CY, Chen JJ, Tsai KH: Quantifying the effectiveness of the sustained muscle stretching treatments in stroke patients with ankle hypertonia. J Electromyogr Kinesiol 2007, 17: 453-461. 10.1016\u002Fj.jelekin.2006.07.001\nDe Luca CJ, Merletti R: Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalogr Clin Neurophysiol 1988, 69: 568-575. 10.1016\u002F0013-4694(88)90169-1\nGravel D, Richards CL, Filion M: Angle dependency in strength measurements of the ankle plantar flexors. Eur J Appl Physiol Occup Physiol 1990, 61: 182-187. 10.1007\u002FBF00357596\nMuramatsu T, Muraoka T, Takeshita D, Kawakami Y, Hirano Y, Fukunaga T: Mechanical properties of tendon and aponeurosis of human gastrocnemius muscle in vivo. J Appl Physiol 2001, 90: 1671-1678.\nHidler JM, Rymer WZ: Limit cycle behavior in spasticity: analysis and evaluation. IEEE Trans Biomed Eng 2000, 47: 1565-1575. 10.1109\u002F10.887937\nKearney RE, Hunter IW: Dynamics of human ankle stiffness: variation with displacement amplitude. J Biomech 1982, 15: 753-756. 10.1016\u002F0021-9290(82)90090-2\nHidler JM, Rymer WZ: A simulation study of reflex instability in spasticity: origins of clonus. IEEE Trans Rehabil Eng 1999, 7: 327-340. 10.1109\u002F86.788469\nMirbagheri MM, Barbeau H, Kearney RE: Intrinsic and reflex contributions to human ankle stiffness: variation with activation level and position. Exp Brain Res 2000, 135: 423-436. 10.1007\u002Fs002210000534\nde Vlugt E, Schouten AC, van der Helm FC: Quantification of intrinsic and reflexive properties during multijoint arm posture. J Neurosci Methods 2006, 155: 328-349. 10.1016\u002Fj.jneumeth.2006.01.022\nOlney SJ, Winter DA: Predictions of knee and ankle moments of force in walking from EMG and kinematic data. J Biomech 1985, 18: 9-20. 10.1016\u002F0021-9290(85)90041-7\nPotvin JR, Norman RW, McGill SM: Mechanically corrected EMG for the continuous estimation of erector spinae muscle loading during repetitive lifting. Eur J Appl Physiol Occup Physiol 1996, 74: 119-132. 10.1007\u002FBF00376504\nBobet J, Norman RW: Least-squares identification of the dynamic relation between the electromyogram and joint moment. J Biomech 1990, 23: 1275-1276. 10.1016\u002F0021-9290(90)90386-H\nPisano F, Miscio G, Del Conte C, Pianca D, Candeloro E, Colombo R: Quantitative measures of spasticity in post-stroke patients. Clin Neurophysiol 2000, 111: 1015-1022. 10.1016\u002FS1388-2457(00)00289-3\nSinkjaer T, Magnussen I: Passive, intrinsic and reflex-mediated stiffness in the ankle extensors of hemiparetic patients. Brain 1994,117(Pt 2):355-363. 10.1093\u002Fbrain\u002F117.2.355\nGorassini MA, Knash ME, Harvey PJ, Bennett DJ, Yang JF: Role of motoneurons in the generation of muscle spasms after spinal cord injury. Brain 2004, 127: 2247-2258. 10.1093\u002Fbrain\u002Fawh243\nNielsen JB, Crone C, Hultborn H: The spinal pathophysiology of spasticity--from a basic science point of view. Acta Physiol (Oxf) 2007, 189: 171-180. 10.1111\u002Fj.1748-1716.2006.01652.x\nLamontagne A, Malouin F, Richards CL, Dumas F: Impaired viscoelastic behaviour of spastic plantarflexors during passive stretch at different velocities. Clin Biomech (Bristol, Avon) 1997, 12: 508-515. 10.1016\u002FS0268-0033(97)00036-3\nBartoo ML, Linke WA, Pollack GH: Basis of passive tension and stiffness in isolated rabbit myofibrils. Am J Physiol 1997, 273: C266-276.\nProske U, Morgan DL: Do cross-bridges contribute to the tension during stretch of passive muscle? J Muscle Res Cell Motil 1999, 20: 433-442. 10.1023\u002FA:1005573625675\nMaganaris CN: In vivo measurement-based estimations of the moment arm in the human tibialis anterior muscle-tendon unit. J Biomech 2000, 33: 375-379. 10.1016\u002FS0021-9290(99)00188-8\nMaganaris CN, Baltzopoulos V, Sargeant AJ: Changes in the tibialis anterior tendon moment arm from rest to maximum isometric dorsiflexion: in vivo observations in man. Clin Biomech (Bristol, Avon) 1999, 14: 661-666. 10.1016\u002FS0268-0033(99)00018-2\nThelen DG: Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. J Biomech Eng 2003, 125: 70-77. 10.1115\u002F1.1531112\nGajdosik RL: Passive extensibility of skeletal muscle: review of the literature with clinical implications. Clin Biomech (Bristol, Avon) 2001, 16: 87-101. 10.1016\u002FS0268-0033(00)00061-9\nEsteki A, Mansour JM: An experimentally based nonlinear viscoelastic model of joint passive moment. J Biomech 1996, 29: 443-450. 10.1016\u002F0021-9290(95)00081-X\nGenadry WF, Kearney RE, Hunter IW: Dynamic relationship between EMG and torque at the human ankle: variation with contraction level and modulation. 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