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This is challenging for users who retain only muscle function above the neck. In this experiment, both able-bodied and motor impairment subjects attended to a flickering stimulus and performed jaw clenches simultaneously. This paper focused on the feasibility of sharing the same collection sites for steady-state visual evoked potentials (SSVEPs) with electromyograms (EMGs) and the potential of building a parallel hybrid BCI system. The results reveal that when the visual stimulation frequency was lower than 20 Hz, there was no serious crosstalk between SSVEP and EMG from jaw clench actions. The EMG signal slightly affects the recognition of SSVEP, while the recognition rate of jaw clench movements based on the mixed signal exceeded 95%. For patients with severe disabilities, the rare applicable EMG signal is facial muscle electrical activity. The proposed study made full use of the combination of jaw clench-related EMG and SSVEP to solve this problem. Only using the same occipital electrodes to simultaneously collect SSVEP with jaw clench-related EMG and classify them could further promote the development and practical application of hybrid BCIs.",{"EN":147,"VI":148},"Detections of Steady-State Visual Evoked Potential and Simultaneous Jaw Clench Action from Identical Occipital Electrodes: A Hybrid Brain-Computer Interface Study","Phát hiện điện thế gợi thị giác trạng thái ổn định và động tác cắn chặt hàm đồng thời từ cùng các điện cực vùng chẩm: Nghiên cứu giao diện não - máy tính lai",{"VOID":150},"Vaughan, T. M., Heetderks, W. J., Trejo, L. J., Rymer, W. Z., Weinrich, M., Noore, M. M., Kubler, A., Dobkin, B. H., Birbaumer, N., Douchin, E., Wolpaw, E. W., & Wolpaw, J. R. (2003). Brain-computer interface technology: A review of the second international meeting. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 11(2), 94–109. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTNSRE.2003.814799\nWolpaw, J. R., & Wolpaw, E. W. (2012). Brain-computer interfaces: Something new under the sun. Oxford University Press.\nWickelgren, I. (2004). Neuroprosthetics. Brain-computer interface adds a new dimension. Science, 306(53), 1878–1879. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.306.5703.1878a\nBin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009). An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. Journal of Neural Engineering, 6(4), 046002. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1741-2560\u002F6\u002F4\u002F046002\nAloise, F., Schettini, F., Aricò, P., Leotta, F., Salinari, S., Mattia, D., Babiloni, F., & Cincotti, F. (2011). P300-based brain-computer interface for environmental control: An asynchronous approach. Journal of Neural Engineering, 8(2), 025025. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1741-2560\u002F8\u002F2\u002F025025\nHwang, H., Kwon, K., & Im, C. (2009). Neurofeedback-based motor imagery training for brain-computer interface (BCI). Journal of Neuroscience Methods, 179(1), 150–156. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jneumeth.2009.01.015\nRebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M. H., & Burdet, E. (2010). A brain controlled wheelchair to navigate in familiar environments. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 590–598. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTNSRE.2010.2049862\nShyu, K., Chiu, Y., Lee, P., Lee, M., Sie, J., Wu, C., Wu, Y., & Tung, P. (2013). Total design of an FPGA-based brain-computer interface control hospital bed nursing system. IEEE Transactions on Industrial Electronics, 60(7), 2731–2739. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTIE.2012.2196897\nPfurtscheller, G., Solis-Escalante, T., Ortner, R., Linortner, P., & Muller-Putz, G. R. (2010). Self-Paced operation of an SSVEP-based orthosis with and without an imagery-based “Brain Switch:” A feasibility study towards a hybrid BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(4), 409–414. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTNSRE.2010.2040837\nAllison, B. Z., Brunner, C., Altstätter, C., Wagner, I. C., Grissmann, S., & Neuper, C. (2012). A hybrid ERD\u002FSSVEP BCI for continuous simultaneous two dimensional cursor control. Journal of Neuroscience Methods, 209(2), 299–307. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jneumeth.2012.06.022\nScherer, R., Müller-Putz, G. R., & Pfurtscheller, G. (2007). Self-initiation of EEG-Based brain-computer communication using the heart rate response. Journal of Neural Engineering, 4(4), L23-29. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1741-2560\u002F4\u002F4\u002FL01\nShinde, N. & George, K. (2016). Brain-controlled driving aid for electric wheelchairs. In 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN), pp. 115–118. https:\u002F\u002Fdoi.org\u002F10.1109\u002FBSN.2016.7516243.\nChai, X., Zhang, Z., Guan, K., Lu, Y., Liu, G., Zhang, T., & Niu, H. (2020). A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis. Biomedical Signal Processing and Control, 56(2), 101687. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.bspc.2019.101687\nLin, K., Cinetto, A., Wang, Y., Chen, X., Gao, S., & Gao, X. (2016). An online hybrid BCI system based on SSVEP and EMG. Journal of Neural Engineering, 13(2), 026020. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1741-2560\u002F13\u002F2\u002F026020\nChang, B. C., & Seo, B. H. (2009). Development of new brain computer interface based on EEG and EMG. In: 2008 IEEE international conference on robotics and biomimetics, pp. 1665–1670. https:\u002F\u002Fdoi.org\u002F10.1109\u002FROBIO.2009.4913251.\nLi, Z., Lei, S., Su, C., & Li, G. (2013). Hybrid brain\u002Fmuscle-actuated control of an intelligent wheelchair. IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013, 19–25. https:\u002F\u002Fdoi.org\u002F10.1109\u002FROBIO.2013.6739429\nShah, M. A., Sheikh, A. A., Sajjad, A. M., & Uppal, M. (2015). A hybrid training-less brain-machine interface using SSVEP and EMG signal. In: 2015 13th international conference on frontiers of information technology (FIT), pp. 93–97. https:\u002F\u002Fdoi.org\u002F10.1109\u002FFIT.2015.26\nGao, Q., Dou, L., Belkacem, A. N., & Chen, C. (2017). Noninvasive electroencephalogram based control of a robotic arm for writing task using hybrid BCI system. BioMed Research International, 2017, 8316485. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2017\u002F8316485\nCosta, Á., Hortal, E., Iáñez, E., & Azorín, J. M. (2014). A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm. PLoS ONE, 9(11), e112352. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0112352\nFoldes, S. T., & Taylor, D. M. (2010). Discreet discrete commands for assistive and neuroprosthetic devices. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(3), 236–244. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTNSRE.2009.2033428\nChen, X., Chen, Z., Gao, S., & Gao, X. (2014). A high-ITR SSVEP-based BCI speller. Brain-computer interfaces, 1(3–4), 181–191. https:\u002F\u002Fdoi.org\u002F10.1080\u002F2326263X.2014.944469\nMa, K., Wang, S., Zhang, S., Sun, Y., & Zheng, D. Z. (2019). Electrode channel optimisation method for steady-state visual evoked potentials. The Journal of Engineering, 2019(23), 8632–8636. https:\u002F\u002Fdoi.org\u002F10.1049\u002Fjoe.2018.9071\nBrainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433–436. https:\u002F\u002Fdoi.org\u002F10.1163\u002F156856897X00357\nChai, X., Zhang, Z., Guan, K., Liu, G., & Niu, H. (2019). A radial zoom motion-based paradigm for steady state motion visual evoked potentials. Frontiers in Human Neuroscience, 13, 127. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffnhum.2019.00127\nEnglehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848–854. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTBME.2003.813539\nGoncharova, I. I., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2003). EMG contamination of EEG: Spectral and topographical characteristics. Clinical Neurophysiology, 114(9), 1580–1593. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1388-2457(03)00093-2\nLeeb, R., Sagha, H., Chavarriaga, R., & Millán, J. R. (2011). A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. Journal of Neural Engineering, 8(2), 025011. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1741-2560\u002F8\u002F2\u002F025011\nHong, J. (2017). Multimodal brain-computer interface combining synchronously electroencephalography and electromyography. Journal of Intelligent & Fuzzy Systems, 33(6), 3355–3362. https:\u002F\u002Fdoi.org\u002F10.3233\u002FJIFS-162104\nChai, X., Zhang, Z., Lu, Y., Liu, G., Zhang, T., & Niu, H. (2019). A hybrid BCI-based environmental control system using SSVEP and EMG signals. 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aim of this study was to determine whether exercises using a balance exercise assist robot (BEAR) improved balance function in older patients with a hip fracture whose ability to perform activities of daily living (ADL) had almost plateaued. Participants were 27 older patients (3 men, 24 women; mean age 81.0 ± 6.3 years) with a hip fracture whose ability to perform ADL had almost plateaued and who were about to be discharged. All participants performed exercises using the BEAR for 20 min a day, 6 days a week, for 2 weeks before leaving the hospital. We assessed the following at pre- and post-exercise: the Timed Up and Go test (TUG), the Berg Balance Scale (BBS), the functional reach test (FRT), the standing test for imbalance and disequilibrium, functional independence measure scores (total and walking ability), preferred gait speed, and muscle strength of the lower extremities. Significant differences were observed between pre- and post-exercise for all measures, including TUG (pre: 21.9 ± 17.7 s, post: 17.4 ± 13.6 s, P \u003C 0.001), BBS (47.0 ± 8.1 points, 50.6 ± 6.3 points, P \u003C 0.001), and FRT (22.4 ± 6.2 cm, 24.8 ± 6.7 cm, P = 0.005). In older patients with hip fracture whose ability to perform ADL has almost plateaued, adding the BEAR exercises to rehabilitation programs could improve balance function better than traditional programs alone. Balance exercises using a robot may be an effective measure to prevent falls at home after a hip fracture.",{"EN":362},"Effects of a Balance Exercise Assist Robot on Older Patients with Hip Fracture: A Preliminary Study",{"VOID":364},"[\"1658277110647208233\"]",{"VOID":366},"Ministry of Health, Labor and Welfare. Individual Matters (Part 5: Rehabilitation). (n.d.). Retrieved December 23, 2019, from https:\u002F\u002Fwww.mhlw.go.jp\u002Ffile\u002F05-Shingikai-12404000-Hokenkyoku-Iryouka\u002F0000182077.pdf.\nNoguchi, Y., Rikimura, S., Hotokezaka, S., Mae, T., Sasaki, K., Iguchi, T., et al. (2011). Length of hospital stay and final discharge destination of hip fracture patients with relation to surgical methods and regional liaison pathway. Orthopedics and Traumatology. https:\u002F\u002Fdoi.org\u002F10.5035\u002Fnishiseisai.60.495.\nMaeshima, S., Osawa, A., Nishio, D., Hirano, Y., & Kigawa, H. (2012). Approaches to hip fractures in convalescent rehabilitation wards—Consideration of length of stay, number of sessions, and discharge destination. Japanese Journal of Comprehensive Rehabilitation Science. https:\u002F\u002Fdoi.org\u002F10.11336\u002Fjjcrs.3.72.\nDi Monaco, M., Vallero, F., De Toma, E., Castiglioni, C., Gardin, L., Giordano, S., et al. (2012). Adherence to recommendations for fall prevention significantly affects the risk of falling after hip fracture: Post-hoc analyses of a quasi-randomized controlled trial. European Journal of Physical and Rehabilitation Medicine, 48(1), 9–15.\nFukushima, T., Sudo, A., & Uchida, A. (2006). Bilateral hip fractures. Journal of Orthopaedic Science. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00776-006-1056-3.\nOzaki, K., Kondo, I., Hirano, S., Kagaya, H., Saitoh, E., Osawa, A., et al. (2017). Training with a balance exercise assist robot is more effective than conventional training for frail older adults. Geriatrics & Gerontology International. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fggi.13009.\nOzaki, K., Kagaya, H., Hirano, S., Kondo, I., Tanabe, S., Itoh, N., et al. (2013). Preliminary trial of postural strategy training using a personal transport assistance robot for patients with central nervous system disorder. Archives of Physical Medicine and Rehabilitation. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.apmr.2012.08.208.\nHirano, S., Saitoh, E., Tanabe, S., Tanikawa, H., Sasaki, S., Kato, D., et al. (2017). The features of Gait Exercise Assist Robot: Precise assist control and enriched feedback. NeuroRehabilitation. https:\u002F\u002Fdoi.org\u002F10.3233\u002FNRE-171459.\nFolstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.\nPodsiadlo, D., & Richardson, S. (1991). The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1532-5415.1991.tb01616.x.\nBerg, K. S. W.-D. J. I., & Gayton, W. D. (1989). Measuring balance in the elderly: Preliminary development of an instrument. Physiotherapy Canada. https:\u002F\u002Fdoi.org\u002F10.3138\u002Fptc.41.6.304.\nDuncan, P. W., Weiner, D. K., Chandler, J., & Studenski, S. (1990). Functional reach: A new clinical measure of balance. Journal of Gerontology. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fgeronj\u002F45.6.m192.\nTeranishi, T., Kondo, I., Sonoda, S., Kagaya, H., Wada, Y., Miyasaka, H., et al. (2010). A discriminative measure for static postural control ability to prevent in-hospital falls: Reliability and validity of the Standing Test for Imbalance and Disequilibrium (SIDE). Japanese Journal of Comprehensive Rehabilitation Science. https:\u002F\u002Fdoi.org\u002F10.11336\u002Fjjcrs.1.11.\nHamilton, B. B., Laughlin, J. A., Fiedler, R. C., & Granger, C. V. (1994). Interrater reliability of the 7-level functional independence measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115–119.\nYardley, L., Beyer, N., Hauer, K., Kempen, G., Piot-Ziegler, C., & Todd, C. (2005). Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age and Ageing. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fageing\u002Fafi196.\nShumway-Cook, A., Brauer, S., & Woollacott, M. (2000). Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Physical Therapy, 80(9), 896–903.\nBohannon, R. W. (1987). Hand-held dynamometry; stability of muscle strength over multiple measurements. Clinical Biomechanics. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0268-0033(87)90131-8.\nGazibara, T., Kurtagic, I., Kisic-Tepavcevic, D., Nurkovic, S., Kovacevic, N., Gazibara, T., et al. (2017). Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatrics. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fpsyg.12217.\nMoore, J. L., Roth, E. J., Killian, C., & Hornby, T. G. (2010). Locomotor training improves daily stepping activity and gait efficiency in individuals poststroke who have reached a “plateau” in recovery. Stroke. https:\u002F\u002Fdoi.org\u002F10.1161\u002FSTROKEAHA.109.563247.\nDemain, S., Wiles, R., Roberts, L., & McPherson, K. (2006). Recovery plateau following stroke: Fact or fiction? Disability and Rehabilitation. https:\u002F\u002Fdoi.org\u002F10.1080\u002F09638280500534796.\nMatsuyama, T., & Yamada, K. (2015). Usefulness of the timed up and go test as an indicator of safety of activities of daily living. Rigakuryoho Kagaku. https:\u002F\u002Fdoi.org\u002F10.1589\u002Frika.30.379.\nGobbens, R. J., & van Assen, M. A. (2014). The prediction of ADL and IADL disability using six physical indicators of frailty: A longitudinal study in the Netherlands. Current Gerontology and Geriatrics Research. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2014\u002F358137.",{"VOID":368},"10.1007\u002Fs40846-020-00568-x","2024-05-14T10:06:20.191+00:00","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs40846-020-00568-x",[372,387,402,415,428,452],{"id":373,"sortIndex":21,"researcher":20,"roles":374,"affiliations":375,"properties":384,"displayName":386,"givenName":20,"familyName":20},"5cea8908-c91c-4a89-895f-30f3ce86598e",[164],[376],{"id":377,"sortIndex":21,"affiliation":378,"properties":20},"b402ee63-2329-4a79-984d-f25951f3f70e",{"id":377,"createTime":20,"updateTime":20,"relativeEntities":379,"slug":20,"properties":380,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":383,"statistic":20},[],{"title":381},{"VI":382},"Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Obu, 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determine the foot kinematics of the depth-jump in healthy adults. We examined the usefulness of a multi-segment foot model for a movement that requires impact absorption and force exertion on the foot. \nTwenty healthy adults (ten men, ten women) performed a depth-jump from a 40-cm height box on to force plates. We analyzed foot motion on the landing and jump preparation phases using the Rizzoli Foot Model. A pattern of foot motion was observed during the depth-jump. Although there were no differences of the foot structure at the static standing position with respect to sex, the maximum angle of the medial longitudinal arch (MLA) in the landing phase was significantly greater in women than in men (p = 0.017). The maximum angle of the MLA in the landing phase was strongly negatively correlated with the jump height (r = − 0.6, p = 0.05). By using a multi-segment foot model, it was possible to observe a common foot motion pattern among subjects, even during a quick movement such as the depth-jump. We suggest that motion analysis using the multi-segment foot model will be useful in evaluating the foot functions of impact absorption and force exertion during a dynamic movement such as the depth-jump.",{"EN":542},"Foot Kinematics of Impact Absorption and Force Exertion During Depth-Jump Using a Multi-segment Foot Model",{"VOID":544},"[\"8733560669051155744\"]",{"VOID":546},"Kaufman, K. R., Brodine, S. K., Shaffer, R. A., Johnson, C. W., & Cullison, T. R. (1999). The effect of foot structure and range of motion on musculoskeletal overuse injuries. American Journal of Sports Medicine, 27(5), 585–593. https:\u002F\u002Fdoi.org\u002F10.1177\u002F03635465990270050701.\nTsai, L. C., Ko, Y. A., Hammond, K. E., Xerogeanes, J. W., Warren, G. L., & Powers, C. M. (2017). Increasing hip and knee flexion during a drop-jump task reduces tibiofemoral shear and compressive forces: Implications for ACL injury prevention training. Journal of Sports Sciences, 35(24), 2405–2411. https:\u002F\u002Fdoi.org\u002F10.1080\u002F02640414.2016.1271138\nIshida, T., Koshino, Y., Yamanaka, M., Ueno, R., Taniguchi, S., Samukawa, M., et al. (2018). The effects of a subsequent jump on the knee abduction angle during the early landing phase. BMC Musculoskeletal Disorders, 19(1), 379. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12891-018-2291-4.\nPaz, G. A., Maia, MdeF., Farias, D., Santana, H., Miranda, H., Lima, V., & Herrington, L. (2016). Kinematic analysis of knee valgus during drop vertical jump and forward step-up in young basketball players. International Journal of Sports Physical Therapy, 11(2), 212–219.\nFukashiro, S., Kurokawa, S., Hay, D. C., & Nagano, A. (2005). Comparison of muscle-tendon interaction of human M. gastrocnemius between ankle- and drop-jumping. International Journal of Sport and Health Science, 3(Special_Issue_2), 253–263. https:\u002F\u002Fdoi.org\u002F10.5432\u002Fijshs.3.253.\nBandholm, T., Boysen, L., Haugaard, S., Zebis, M. K., & Bencke, J. (2008). Foot medial longitudinal-arch deformation during quiet standing and gait in subjects with medial tibial stress syndrome. Journal of Foot and Ankle Surgery, 47(2), 89–95. https:\u002F\u002Fdoi.org\u002F10.1053\u002Fj.jfas.2007.10.015\nBennett, J. E., Reinking, M. F., Pluemer, B., Pentel, A., Seaton, M., & Killian, C. (2001). Factors contributing to the development of medial tibial stress syndrome in high school runners. Journal of Orthopaedic and Sports Physical Therapy, 31(9), 504–510. https:\u002F\u002Fdoi.org\u002F10.2519\u002Fjospt.2001.31.9.504.\nCarson, M. C., Harrington, M. E., Thompson, N., O’Connor, J. J., & Theologis, T. N. (2001). Kinematic analysis of a multi-segment foot model for research and clinical applications: A repeatability analysis. Journal of Biomechanics, 34(10), 1299–1307. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0021-9290(01)00101-4.\nSimon, J., Doederlein, L., McIntosh, A. S., Metaxiotis, D., Bock, H. G., & Wolf, S. I. (2006). The Heidelberg foot measurement method: Development, description and assessment. Gait and Posture, 23(4), 411–424. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.gaitpost.2005.07.003.\nLeardini, A., Benedetti, M. G., Berti, L., Bettinelli, D., Nativo, R., & Giannini, S. (2007). Rear-foot, mid-foot and fore-foot motion during the stance phase of gait. Gait and Posture, 25(3), 453–462. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.gaitpost.2006.05.017.\nBoden, B. P., Dean, C. S., Feagin, J. A., & Garrett, W. E. (2000). Mechanisms of anterior cruciate ligament injury. Orthopedics, 23(6), 573–578. https:\u002F\u002Fdoi.org\u002F10.3928\u002F0147-7447-20000601-15.\nShimokochi, Y., & Shultz, S. J. (2008). Mechanisms of noncontact anterior cruciate ligament injury. Journal of Athletic Training. https:\u002F\u002Fdoi.org\u002F10.4085\u002F1062-6050-43.4.396\nPortinaro, N., Leardini, A., Panou, A., Monzani, V., & Caravaggi, P. (2014). Modifying the Rizzoli foot model to improve the diagnosis of pes-planus: Application to kinematics of feet in teenagers. Journal of Foot and Ankle Research, 7(1), 1–7. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13047-014-0057-2.\nPowell, D. W., Long, B., Milner, C. E., & Zhang, S. (2011). Frontal plane multi-segment foot kinematics in high- and low-arched females during dynamic loading tasks. Human Movement Science, 30(1), 105–114. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.humov.2010.08.015.\nELFTMAN, H. (1960). The transverse tarsal joint and its control. Clinical Orthopaedics, 16, 41–46.\nKirby, K. A. (2017). Longitudinal arch load-sharing system of the foot. Revista Española de Podología, 28(1), e18–e26. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.repod.2017.03.003.\nHicks, J. H. (1954). The mechanics of the foot. II. The plantar aponeurosis and the arch. Journal of Anatomy, 88(1), 25–30.\nShultz, S. J., Shimokochi, Y., Nguyen, A. D., Schmitz, R. J., Beynnon, B. D., & Perrin, D. H. (2007). Measurement of varus-valgus and internal-external rotational knee laxities in vivo—Part II: Relationship with anterior-posterior and general joint laxity in males and females. Journal of Orthopaedic Research, 25(8), 989–996. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjor.20398\nTakabayashi, T., Edama, M., Inai, T., & Kubo, M. (2018). Sex-related differences in coordination and variability among foot joints during running. Journal of Foot and Ankle Research, 11(1), 1–8. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13047-018-0295-9\nWilkerson, R. D., & Mason, M. A. (2000). Differences in men’s and women’s mean ankle ligamentous laxity. The Iowa Orthopaedic Journal, 20, 46–48.\nZifchock, R. A., Davis, I., Hillstrom, H., & Song, J. (2006). The effect of gender, age, and lateral dominance on arch height and arch stiffness. Foot and Ankle International, 27(5), 367–372. https:\u002F\u002Fdoi.org\u002F10.1177\u002F107110070602700509.\nTaunton, J. E., Ryan, M. B., Clement, D. B., McKenzie, D. C., Lloyd-Smith, D. R., & Zumbo, B. D. (2002). A retrospective case-control analysis of 2002 running injuries. British Journal of Sports Medicine, 36(2), 95–101. https:\u002F\u002Fdoi.org\u002F10.1136\u002Fbjsm.36.2.95.\nde César, P. C., de Alves, J. A. O., & Gomes, J. L. E. (2014). Height of the foot longitudinal arch and anterior cruciate ligament injuries. Acta Ortopedica Brasileira, 22(6), 312–314.\nPrapavessis, H., & McNair, P. J. (1999). Effects of instruction in jumping technique and experience jumping on ground reaction forces. Journal of Orthopaedic and Sports Physical Therapy, 29(6), 352–356. https:\u002F\u002Fdoi.org\u002F10.2519\u002Fjospt.1999.29.6.352",{"VOID":548},"10.1007\u002Fs40846-020-00560-5","2024-05-16T02:32:58.103+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40846-020-00560-5",[552,567,584,601,614,636],{"id":553,"sortIndex":21,"researcher":20,"roles":554,"affiliations":555,"properties":564,"displayName":566,"givenName":20,"familyName":20},"a7df7992-9ea4-4106-88ed-f8a925e53f79",[164],[556],{"id":557,"sortIndex":21,"affiliation":558,"properties":20},"236cce40-f225-4789-b477-44588e030fd7",{"id":557,"createTime":20,"updateTime":20,"relativeEntities":559,"slug":20,"properties":560,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":563,"statistic":20},[],{"title":561},{"VI":562},"Graduate Course of Health and Social Services, Graduate School of Saitama Prefectural University, Saitama, Japan",[],{"title":565},{"VI":566},"Yuka Sekiguchi",{"id":568,"sortIndex":176,"researcher":20,"roles":569,"affiliations":570,"properties":579,"displayName":581,"givenName":20,"familyName":20},"65de8768-4a6d-4356-a4d5-bef0af395359",[164],[571],{"id":572,"sortIndex":21,"affiliation":573,"properties":20},"ee8cbeca-8366-45d3-bcee-56caf76b9738",{"id":572,"createTime":20,"updateTime":20,"relativeEntities":574,"slug":20,"properties":575,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":578,"statistic":20},[],{"title":576},{"VI":577},"Department of Health and Social Services, Saitama Prefectural University, Saitama, Japan",[],{"title":580,"gsAuthor":582},{"VI":581},"Takanori Kokubun",{"VOID":583},"[\"RLTqrUgAAAAJ\"]",{"id":585,"sortIndex":203,"researcher":20,"roles":586,"affiliations":587,"properties":596,"displayName":598,"givenName":20,"familyName":20},"ce636601-6da2-4f5c-9b61-f0362cdd738d",[164],[588],{"id":589,"sortIndex":21,"affiliation":590,"properties":20},"32d16f19-ea17-4706-b423-9b46ab603e86",{"id":589,"createTime":20,"updateTime":20,"relativeEntities":591,"slug":20,"properties":592,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":595,"statistic":20},[],{"title":593},{"VI":594},"Department of Rehabilitation, Faculty of Health Science, University of Human Arts and Sciences, Saitama, Japan",[],{"title":597,"gsAuthor":599},{"VI":598},"Hiroki 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the rapid development of wearable technology, wearable medical devices have gradually garnered a significant amount of research interest. Motion reconstruction can accurately reproduce the posture of the user at the time of the accident, which provides medical personnel with necessary reference information. However, because of the vast range of human body activities, motion reconstruction needs high-frequency sampling data to avoid the occurrence of errors. Moreover, the fact that movements resulting from an accident can be irregular, the difficulties arising from unexpected training samples. This study attempts to establish a real-time human body inferential motion reconstruction system on fall accident. The data of human motion is recorded by using tri-axis accelerometers and tri-axis gyroscopes. The angles and tracks of the human limbs computed, and the next action occurrence point deduced using long short-term memory. Then the postural trajectory is corrected using feedback inference of gravity data from the end of a fall accident. Through the correction mechanism of bidirectional feedback, the error diffusion caused can reduce efficiency. In this study, using a parameter adjustment strategy under data sampling rate of 0.01 s, the average normal-m reconstruction rate, as well as the fall-motion reconstruction rate, can be determined. The overall posture is reproduced through the 3D video to ambulance personnel as a reference.",{"EN":716},"Inferential Motion Reconstruction of Fall Accident Based on LSTM Neural Network",{"VOID":718},"[\"8047522947262700910\"]",{"VOID":720},"Fonad, E., Wahlin, T. B., Winblad, B., Emami, A., & Sandmark, H. (2008). Falls and fall risk among nursing home residents. Journal of Clinical Nursing, 17(1), 126–134.\nLaessoe, U., Hoeck, H. C., Simonsen, O., Sinkjaer, T., & Voigt, M. (2007). Fall risk in an active elderly population-can it be assessed? Jounal of Negative Results in Biomedicine, 6, 2–13.\nKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems 25.\nHochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.\nSposaro, F., & Tyson, G. (2009). iFall: An android application for fall monitoring and response. In Proceeding of annual international conference of the IEEE engineering in medicine and biology society, Minneapolis, USA\nNyana, M. N., Taya, Francis E. H., & Murugasuc, E. (2008). A wearable system for pre-impact fall detection. Journal of Biomechanics, 41(16), 3475481.\nDai, J., Bai, X., Yang, Z., Shen, Z., & Xuan, D. (2010). PerFallD: A pervasive fall detection system using mobile phones. In Proceeding of 8th IEEE international conference on pervasive computing and communications workshops, Mannheim Germany.\nLin, C. S., Hsu, H. C., Chiu, C. C., Lin, S. L., & Chao, C. S. (2006). A PDA based wearable system for real-time monitoring of human falls. IETE Journal of Research, 52(6), 403–416.\nLindemann, U., Hock, A., & Stuber, M. (2005). Evaluation of a fall detector based on accelerometers: A pilot study. Medical & Biological Engineering & Computing, 43(5), 548–551.\nYang, C. C., & Hsu, Y. L. (2006). Development of a portable system for physical activity assessment in a home environment. In Proceeding of international computer symposium (pp. 1339–1344), Taipei, Taiwan.\nWang, C. C., Chiang, C. Y., Lin, P. Y., Chou, Y. C., Kuo, I.-T., Huang, C. N., & Chan, C. T. (2008) Development of a fall detecting system for the elderly residents. In Proceeding of the 2nd international conference on bioinformatics and biomedical engineering (pp. 1359–1362), Shanghai, China\nDelahoz, Y. S., & Labrador, M. A. (2014). Survey on fall detection and fall prevention using wearable and external sensors. Sensors, 14(10), 19806–19842.\nShi, Y., Shi, Y. C., & Wang, X. (2012). Fall detection on mobile phones using features from a five-phase model. In Proceeding of 9th International Conference on Ubiquitous Intelligence and Computing and Autonomic and Trusted Computing (pp. 951–956), Fukuoka, Japan\nSengto, A., & Leauhatong, T. (2012). Human falling detection algorithm using back propagation neural network. In Proceeding of 5th Biomedical Engineering International Conference (pp. 1–5). Thailand: Ubon Ratchathani.\nZhang, C., Lai, C. F., Lai, Y. H., Wu, Z. W., & Chao, H. C. (2017). An inferential real-time falling posture reconstruction for Internet of healthcare things. Journal of Network and Computer Applications, 89, 86–95.\nChiang, J. C. (2009). Adaptive collaborative multi-sensor devices to detect body position. Master’s Thesis of Department of Engineering Sciences, National Cheng Kung University.\nGraves, A., & Schmidhuber, J. (2009). Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in neural information processing systems (pp. 1–8)\nGraves, A., Mohamed, A.-R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6645–6649), Vancouver, BC, Canada, May 2013.\nZhou, C., Sun, C., Liu, Z., & Lau, F. (2015). A C-LSTM neural network for text classification. arXiv:1511.08630v2\nChen, Y., Cheng, B., & Cheng, X. (2016). Food safety document classification using LSTM-based ensemble learning. Revista Técnica de la Facultad de Ingeniería Universidad del Zulia, 39(10), 172–178.\nRubinstein, R. Y., & Kroese, D. P. (2004). The cross-entropy method: A unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning. New York: Springer.\nBottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010 (pp. 177–186). New York: Springer.\nRuder, S. (2016). An overview of gradient descent optimization algorithms. CoRR arXiv:1609.04747\nRiedmiller, M., Braun, H. (1992). RPROP-A fast adaptive learning algorithm. In Proceedings of the ISCIS VII, Universitat.\nSrivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.\nJian G., & Stephen G. (2016). Depth Dropout: Efficient Training of Residual Convolutional Neural Networks. In Proceeding of 2016 International Conference on Digital Image Computing: Techniques and Applications (pp. 1–7). Gold Coast, QLD, Australia, Dec.\nHecht-Nielsen, R. (1988). Theory of the backpropagation neural network. Neural Networks, 1, 445.",{"VOID":722},"10.1007\u002Fs40846-018-0401-2","2024-05-10T09:08:46.311+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40846-018-0401-2",[726,741,754],{"id":727,"sortIndex":21,"researcher":20,"roles":728,"affiliations":729,"properties":738,"displayName":740,"givenName":20,"familyName":20},"aa97848d-79d3-4faa-89e7-812c3746433c",[164],[730],{"id":731,"sortIndex":21,"affiliation":732,"properties":20},"04f225f7-d1c4-45d9-9a7d-de7291532ff0",{"id":731,"createTime":20,"updateTime":20,"relativeEntities":733,"slug":20,"properties":734,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":737,"statistic":20},[],{"title":735},{"VI":736},"Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan, ROC",[],{"title":739},{"VI":740},"Yao-Chung Chang",{"id":742,"sortIndex":176,"researcher":20,"roles":743,"affiliations":744,"properties":751,"displayName":753,"givenName":20,"familyName":20},"fbebca4c-c039-44ad-b389-b81cef969d91",[164],[745],{"id":731,"sortIndex":21,"affiliation":746,"properties":20},{"id":731,"createTime":20,"updateTime":20,"relativeEntities":747,"slug":20,"properties":748,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":750,"statistic":20},[],{"title":749},{"VI":736},[],{"title":752},{"VI":753},"Ying Hsun Lai",{"id":755,"sortIndex":203,"researcher":20,"roles":756,"affiliations":757,"properties":766,"displayName":768,"givenName":20,"familyName":20},"1a6116e9-aa92-44b8-8d9a-6be523bff974",[164],[758],{"id":759,"sortIndex":21,"affiliation":760,"properties":20},"07168b73-a710-4e34-8df1-c029c5fc1e50",{"id":759,"createTime":20,"updateTime":20,"relativeEntities":761,"slug":20,"properties":762,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":765,"statistic":20},[],{"title":763},{"VI":764},"Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan, ROC",[],{"title":767,"gsAuthor":769},{"VI":768},"Tien-Chi Huang",{"VOID":770},"[\"jfEghUYAAAAJ\"]",{"url":724,"publisher":772,"properties":818},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":773,"slug":10,"properties":774,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":778,"manageAffiliations":787,"indexDatabases":798,"url":20,"thumbnailPath":20,"statistic":813,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":775,"title":776,"eissn":777},{"VOID":13},{"EN":15},{"VOID":17},[779,783],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":780,"label":781,"description":782,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":784,"label":785,"description":786,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[788,793],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":789,"slug":20,"properties":790,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":792,"statistic":20},[],{"title":791},{"EN":41},[43],{"id":45,"createTime":20,"updateTime":20,"relativeEntities":794,"slug":20,"properties":795,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":797,"statistic":20},[],{"title":796},{"EN":49},[43],[799,806],{"id":53,"indexDatabase":800,"url":64,"indexYears":65,"academicFieldIds":805,"indexDatabaseRanking":69},{"id":55,"createTime":20,"updateTime":20,"relativeEntities":801,"label":802,"description":803,"key":61,"publicationTags":804,"standard":20},[],{"EN":58,"VI":58},{"EN":58,"VI":60},[63],[67,68],{"id":71,"indexDatabase":807,"url":84,"indexYears":20,"academicFieldIds":812,"indexDatabaseRanking":20},{"id":73,"createTime":20,"updateTime":20,"relativeEntities":808,"label":809,"description":810,"key":80,"publicationTags":811,"standard":20},[],{"EN":76,"VI":76},{"EN":78,"VI":79},[82,83],[86],{"impactFactor":21,"impactFactorByYear":814,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":815,"totalCitation":111,"totalCitationByYear":816,"totalCitationPerPublication":121,"totalCitationPerPublicationByYear":817,"hindexLast5Year":132,"hindex":132},{"2016":89,"2017":90,"2018":91,"2019":92,"2020":93,"2021":94,"2022":95,"2023":96},{"2015":101,"2016":102,"2017":103,"2018":104,"2019":105,"2020":106,"2021":107,"2022":108,"2023":109,"2024":110},{"2015":113,"2016":114,"2017":115,"2018":116,"2019":117,"2020":118,"2021":119,"2022":120,"2023":98},{"2015":123,"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":130,"2023":131},{"pages":819,"volume":821},{"VOID":820},"315-328",{"VOID":822},"39",{"total":224,"publishYear":824,"statisticByYear":825},2018,{"2019":176,"2020":176,"2025":176},"2018-03-31","ERROR_IN_ANALYZE_CITATION","2026-07-21T16:59:17.010+00:00",[69,82],{"id":831,"createTime":832,"updateTime":833,"relativeEntities":834,"slug":835,"properties":836,"entityType":153,"verifyStatus":154,"verifyTime":847,"verifyNote":156,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":848,"fullTextUrl":20,"authors":849,"publicationType":295,"publisherRelationship":897,"citationCount":949,"citationInfo":950,"publishDate":953,"publishYear":951,"citationAnalyzeStatus":19,"lastCitationAnalyze":833,"indexDatabases":954,"openAccess":20,"references":20,"isForceReanalyzing":351},"9016ba8c-3a2e-4330-811f-19192f0fd2c4","2024-02-13T00:05:24.754+00:00","2026-07-20T16:04:50.959+00:00",[],"Extraction-of-Microaneurysms-and-Hemorrhages-from-Digital-Retinal-Images",{"abstract":837,"title":839,"gsPaper":841,"references":843,"doi":845},{"EN":838},"Detection of red lesions from color fundus images is crucial in the early detection of diabetic retinopathy. Automatic red lesion detection is a challenging task as they have low contrast, irregular shapes, variable sizes and resemblance of their intensities with blood vessels. This paper presents a novel hybrid red lesion detection system that combines phase congruency based and mathematical morphology based methods to detect candidate red lesions. The significant contribution of this paper is the computation of phase congruency using extended 2D log gabor filter. The proposed red lesion detection system is a three stage system which combines polynomial contrast enhancement for preprocessing, hybrid detection for coarse candidate red lesion extraction, and support vector machine classifier for fine segmentation of red lesions. Experimental evaluations of the proposed system using publicly available fundus image databases demonstrates superior performance over other red lesion detection algorithms recently reported in the literature.",{"EN":840},"Extraction of Microaneurysms and Hemorrhages from Digital Retinal Images",{"VOID":842},"[\"3317113661951459913\"]",{"VOID":844},"World health organization: prevention of blindness and visual impairment. http:\u002F\u002Fwww.who.int\u002Fblindness\u002Fcauses\u002Fpriority\u002Fen\u002Findex8.html.\nKlonoff, D. C., & Schwartz, D. M. (2000). An economic analysis of interventions for diabetes. Diabetes Care, 23, 390–404.\nYau, J. W., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., et al. (2012). Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care., 35, 556–564.\nAlghadyan, A. A. (2011). Diabetic retinopathy-an update. Saudi Journal of Ophthalmology, 25, 99–111.\nNunes, S., Pires, I., Rosa, A., Duarte, L., Bernardes, R., & Cunha-Vaz, J. (2009). Microaneurysm turnover is a biomarker for diabetic retinopathy progression to clinically significant macular edema: Findings for type 2 diabetics with non-proliferative retinopathy. Ophthalmologica, 223, 292–297.\nSpencer, T., Olson, J., McHardy, K., Sharp, P., & Forrester, J. (1996). An image processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Computers and Biomedical Research, 29, 284–302.\nFrame, A., Undrill, P., Cree, M., Olson, J., McHardy, K., Sharp, P., et al. (1998). A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Computers in Biology and Medicine, 28, 225–238.\nVincent, L. (1992). Morphological area openings and closings for grey scale images. In Proceedings of NATO shape in picture workshop (pp. 197–208). New York: Springer.\nMookiah, M. R. K., Acharya, U. R., Chua, C. K., Lim, C. M., Ng, E., & Laude, A. (2013). Computer-aided diagnosis of diabetic retinopathy: A review. Computers in Biology and Medicine, 43, 2136–2155.\nAcharya, U. R., Ng, E. Y. K., Tan, J. H., Sree, S. V., & Ng, K. H. (2012). An integrated index for the identification of diabetic retinopathy stages using texture parameters. Journal of Medical Systems, 36, 2011–2020.\nKahai, P., Namuduri, K. R., & Thompson, H. (2006). A decision support framework for automated screening of diabetic retinopathy. International Journal of Biomedical Imaging, 2006, 1–8.\nTavakoli, M., Shahri, R. P., Pourreza, H., Mehdizadeh, A., Banaee, T., & Toosi, M. H. B. (2013). A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recognition, 46, 2740–2753.\nNiemeijer, M., Ginneken, B. V., Staal, J., Suttorp-Schulton, M. S., & Abramoff, M. D. (2005). Automatic detection of red lesions in digital color fundus photograph. IEEE Transactions on Medical Imaging, 24, 584–592.\nAkram, M. U., Khalid, S., & Khan, S. A. (2013). Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognition, 46, 107–116.\nLazar, I., & Hajdu, A. (2013). Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE Transactions on Medical Imaging, 32, 400–407.\nRam, K., Joshi, G. D., & Sivaswamy, J. (2011). A successive clutter-rejection-based approach for early detection of diabetic retinopathy. IEEE Transactions on Biomedical Engineering, 58, 664–673.\nZhang, B., Karray, F., Li, Q., & Zhang, L. (2012). Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction. Information Sciences, 200, 78–90.\nSopharak, A., Uyyanonvara, B., & Barman, S. (2013). Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images. Computerized Medical Imaging and Graphics, 37, 394–402.\nFleming, A., Philip, S., Goatman, K., Olson, J., & Sharp, P. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Transactions on Medical Imaging, 25, 1223–1232.\nAntal, B., & Hajdu, A. (2012). An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Transactions on Biomedical Engineering, 59, 1720–1726.\nKande, G. B., Savithri, T. S., & Subbaiah, P. V. (2010). Automatic detection of microaneurysms and hemorrhages in digital fundus images. Journal of Digital Imaging, 23, 430–437.\nZhang, B., Wu, X., Yo, J., Li, Q., & Karray, F. (2010). Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recognition, 43, 2237–2248.\nQuellec, G., Stephen, R., & Abramoff, M. D. (2011). Optimal filter framework for automated, instantaneous detection of lesions in retinal images. IEEE Transactions on Medical Imaging, 30, 523–533.\nSeoud, L., Hurtut, T., Chelbi, J., Cheriet, F., & Langlois, J. M. P. (2016). Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Transactions on Medical Imaging, 35, 1116–1126.\nQuellec, G., Lamard, M., Josselin, P. M., Cazuguel, G., Cochener, B., & Roux, C. (2008). Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Transactions on Medical Imaging, 27, 1230–1241.\nLiesenfeld, B., Kohner, E., Piehlmeier, W., Kluthe, S., Porta, M., Bek, T., et al. (2000). A telemedical approach to the screening of diabetic retinopathy, Digital fundus photography. Diabetes Care, 23, 345–348.\nWalter, T., Massin, P., Erginary, A., Ordonez, R., Jeulin, C., & Klein, J. (2007). Automatic detection of microaneurysms in color fundus images. Medical Image Analysis, 11, 555–566.\nTagore, M.R.N., Kande, G.B., Rao, E.V.K., & Rao, B.P. (2013). Segmentation of retinal vasculature using phase congruency and hierarchical clustering. In Proceedings of the 2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2013) (pp. 361–366).\nE. T. D. R. S. R. Group. (1991). Grading diabetic retinopathy from stereoscopic color fundus photographs–an extension of the modified airlie house classification. Ophthalmology, 98, 786–806.\nCover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.\nBurges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.\nKauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., et al. (2005). DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms, Technical Report.\nKauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., et al. (2006). DIARETDB1 diabetic retinopathy database and evaluation protocol, Technical Report.",{"VOID":846},"10.1007\u002Fs40846-017-0237-1","2024-06-23T04:35:26.440+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40846-017-0237-1",[850,865,880],{"id":851,"sortIndex":21,"researcher":20,"roles":852,"affiliations":853,"properties":862,"displayName":864,"givenName":20,"familyName":20},"ab5705e9-52f3-4902-97a7-1d1770663f4e",[164],[854],{"id":855,"sortIndex":21,"affiliation":856,"properties":20},"567f3b48-0ecb-47e0-a2f6-46ebeb7fd6d1",{"id":855,"createTime":20,"updateTime":20,"relativeEntities":857,"slug":20,"properties":858,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":861,"statistic":20},[],{"title":859},{"VI":860},"Department of Electronics & Communication Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, India",[],{"title":863},{"VI":864},"Ravindranath Tagore Mamilla",{"id":866,"sortIndex":176,"researcher":20,"roles":867,"affiliations":868,"properties":877,"displayName":879,"givenName":20,"familyName":20},"937ef1e4-9e3c-474d-a9d2-b0254417e929",[164],[869],{"id":870,"sortIndex":21,"affiliation":871,"properties":20},"0217a4cc-0e93-487b-97cd-ac24d527582a",{"id":870,"createTime":20,"updateTime":20,"relativeEntities":872,"slug":20,"properties":873,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":876,"statistic":20},[],{"title":874},{"VI":875},"Department of Electronics & Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, India",[],{"title":878},{"VI":879},"Venkata Krishna Rao Ede",{"id":881,"sortIndex":203,"researcher":20,"roles":882,"affiliations":883,"properties":892,"displayName":894,"givenName":20,"familyName":20},"0cea36c1-8c9a-4d69-8863-8fd95e3589cc",[164],[884],{"id":885,"sortIndex":21,"affiliation":886,"properties":20},"4d73acae-7afc-4712-b8a6-f1aaad4e3a11",{"id":885,"createTime":20,"updateTime":20,"relativeEntities":887,"slug":20,"properties":888,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":891,"statistic":20},[],{"title":889},{"VI":890},"Department of Electronics & Communication Engineering, JNTU, Kakinada, India",[],{"title":893,"gsAuthor":895},{"VI":894},"Prabhakar Rao Bhima",{"VOID":896},"[\"rDO94ZAAAAAJ\"]",{"url":848,"publisher":898,"properties":944},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":899,"slug":10,"properties":900,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":904,"manageAffiliations":913,"indexDatabases":924,"url":20,"thumbnailPath":20,"statistic":939,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":901,"title":902,"eissn":903},{"VOID":13},{"EN":15},{"VOID":17},[905,909],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":906,"label":907,"description":908,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":910,"label":911,"description":912,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[914,919],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":915,"slug":20,"properties":916,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":918,"statistic":20},[],{"title":917},{"EN":41},[43],{"id":45,"createTime":20,"updateTime":20,"relativeEntities":920,"slug":20,"properties":921,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":923,"statistic":20},[],{"title":922},{"EN":49},[43],[925,932],{"id":53,"indexDatabase":926,"url":64,"indexYears":65,"academicFieldIds":931,"indexDatabaseRanking":69},{"id":55,"createTime":20,"updateTime":20,"relativeEntities":927,"label":928,"description":929,"key":61,"publicationTags":930,"standard":20},[],{"EN":58,"VI":58},{"EN":58,"VI":60},[63],[67,68],{"id":71,"indexDatabase":933,"url":84,"indexYears":20,"academicFieldIds":938,"indexDatabaseRanking":20},{"id":73,"createTime":20,"updateTime":20,"relativeEntities":934,"label":935,"description":936,"key":80,"publicationTags":937,"standard":20},[],{"EN":76,"VI":76},{"EN":78,"VI":79},[82,83],[86],{"impactFactor":21,"impactFactorByYear":940,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":941,"totalCitation":111,"totalCitationByYear":942,"totalCitationPerPublication":121,"totalCitationPerPublicationByYear":943,"hindexLast5Year":132,"hindex":132},{"2016":89,"2017":90,"2018":91,"2019":92,"2020":93,"2021":94,"2022":95,"2023":96},{"2015":101,"2016":102,"2017":103,"2018":104,"2019":105,"2020":106,"2021":107,"2022":108,"2023":109,"2024":110},{"2015":113,"2016":114,"2017":115,"2018":116,"2019":117,"2020":118,"2021":119,"2022":120,"2023":98},{"2015":123,"2016":124,"2017":125,"2018":126,"2019":127,"2020":128,"2021":129,"2022":130,"2023":131},{"pages":945,"volume":947},{"VOID":946},"395-408",{"VOID":948},"37",17,{"total":949,"publishYear":951,"statisticByYear":952},2017,{"2018":203,"2020":203,"2021":203,"2022":261,"2023":203,"2024":224,"2025":176},"2017-03-24",[69,82],{"id":956,"createTime":957,"updateTime":958,"relativeEntities":959,"slug":960,"properties":961,"entityType":153,"verifyStatus":154,"verifyTime":972,"verifyNote":156,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":973,"fullTextUrl":20,"authors":974,"publicationType":295,"publisherRelationship":1063,"citationCount":949,"citationInfo":1115,"publishDate":1118,"publishYear":1116,"citationAnalyzeStatus":19,"lastCitationAnalyze":1119,"indexDatabases":1120,"openAccess":20,"references":20,"isForceReanalyzing":351},"53577dc5-ece0-4aa3-810b-5a72bf0a94a2","2023-12-13T10:12:28.006+00:00","2026-07-16T01:39:56.902+00:00",[],"Age-related-Changes-in-Dynamic-Postural-Control-Ability-in-the-Presence-of-Sensory-Perturbation",{"abstract":962,"title":964,"gsPaper":966,"references":968,"doi":970},{"EN":963},"For the development of fall prevention programs, the dynamic postural control of elderly persons under conditions when their senses are perturbed needs to be investigated. The present study investigates the manner in which elderly persons respond to external perturbations when their visual and somatic senses are disturbed. The subjects included 4 healthy older adults and 6 young adults. In the experiment, external perturbation was introduced through a platform that was movable in four directions (forward, backward, right, and left). The effects of sensory disturbance created by wearing translucent goggles and standing on a soft floor were examined. The responses were measured in terms of the center of pressure (COP) on the force plate, the electromyographic (EMG) activity, and the joint angle from video analysis. The COP analysis showed that the older group, especially in the presence of sensory disturbances, required a longer time than that for the younger group to return to an erect standing position after external perturbations (recovery time). The EMG indicated that the older group used the articular muscles of the knee to respond to postural perturbations involving up-and-down movements. The recovery time is a characteristic parameter of the response to external perturbations in the presence of sensory disturbances, and thus a potentially useful indicator in evaluating balance ability. The increases in knee muscle’ activities were due to reduced ankle joint torque, which is presumably one of the causes of the prolonged recovery time. These findings could be applied to the development of fall prevention training.",{"EN":965},"Age-related Changes in Dynamic Postural Control Ability in the Presence of Sensory Perturbation",{"VOID":967},"[\"13198159351880467728\"]",{"VOID":969},"Shapiro, A., & Melzer, I. (2010). Balance perturbation system to improve balance compensatory responses during walking in old persons. Journal of NeuroEngineering and Rehabilitation, 7, 32.\nMaeda, Y., Tanaka, T., Nakajima, Y., & Shimizu, K. (2011). Analysis of postural adjustment responses to perturbation stimulus by surface tilts in the feet-together position. Journal of Medical and Biological Engineering, 31, 301–305.\nHorak, F. B., Nashner, L. M., & Diener, H. C. (1990). Postural strategies associated with somatosensory and vestibular loss. Experimental Brain Research, 82, 167–177.\nMeyer, P. F., Oddsson, L. E., & De Luca, C. J. (2004). The role of plantar cutaneous sensation in unperturbed stance. Experimental Brain Research, 156, 505–512.\nJeka, J. J., Allison, L. K., & Kiemel, T. (2010). The dynamics of visual reweighting in healthy and fall-prone older adults. Journal of Motor Behavior, 42, 197–208.\nY. Maeda, T. Toshiaki, Y. Nakajima, T. Miyasaka, T. Izumi and N. Kato (2100). Dynamic postural adjustments in stance in response to translational perturbation in the presence of visual and somatosensory disturbance. Journal of Medical and Biological Engineering (in press).\nBohannon, R. W. (1986). Test-retest reliability of hand-held dynamometry during a single session of strength assessment. Physical Therapy, 66, 206–209.\nFortiedr, P. A. (1994). Use of spike triggered of muscle activity to quantify inputs to motoneuron pools. Journal of Neurophysiology, 72, 248–265.\nEnoka, R. M. (1996). Eccentric contractions require unique activation strategies by the nervous system. Journal of Applied Physiology, 81, 2339–2346.\nHortobágyi, T., Olmo, M. F., & Rothwell, J. C. (2006). Age reduces cortical reciprocal inhibition in humans. Experimental Brain Research, 171, 322–329.\nMaki, B. E., & Ostrovski, G. (1993). Scaling of postural responses to transient and continuous perturbation. Gait Posture, 1, 93–104.\nMaki, B. E., & Fernie, G. R. (1988). A system identification approach to balance testing. Progress in Brain Research, 76, 297–306.\nManchester, D., Woollacott, M., Zederbauer-Hylton, N., & Marin, O. (1989). Visual, vestibular and somatosensory contributions to balance control in the older adults. Journals of Gerontology, 44, M118–M127.\nGu, M. J., Schultz, A. B., Shepard, N. T., & Alexander, N. B. (1996). Postural control in young and elderly adults when stance is perturbed: dynamics. Journal of Biomechanics, 29, 319–329.\nHorak, F. B., Shupert, C. L., & Mirka, A. (1989). Components of postural dyscontrol in the elderly: review. Neurobiology of Aging, 10, 727–738.",{"VOID":971},"10.1007\u002Fs40846-015-0009-8","2024-05-17T04:42:59.349+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs40846-015-0009-8",[975,990,1003,1018,1033,1048],{"id":976,"sortIndex":21,"researcher":20,"roles":977,"affiliations":978,"properties":987,"displayName":989,"givenName":20,"familyName":20},"c75e93b0-7f00-4824-8bab-b2119a26a6df",[164],[979],{"id":980,"sortIndex":21,"affiliation":981,"properties":20},"452b5971-efd6-43f6-8684-fcc4613d959e",{"id":980,"createTime":20,"updateTime":20,"relativeEntities":982,"slug":20,"properties":983,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":986,"statistic":20},[],{"title":984},{"VI":985},"Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan",[],{"title":988},{"VI":989},"Yusuke 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improvement of environmental quality is aligned with the betterment of life quality. Poor air quality has a greatest impact on people health, it links to cancer, long-term harm to cardiovascular and respiratory systems. Conversely, safe air quality free of harmful gases such as formaldehyde, volatile organic compounds and carbon monoxide helps to prevent disease and other health problems. The application of information technology can greatly enhance the effectiveness of ensuring good air quality. Therefore, the implementation of environmental quality and harmful gases monitoring system is beneficial to manage indoor air quality. In this work, we built an environment quality monitoring system, which can adjust the indoor air quality and monitor the concentration of formaldehyde, volatile organic compounds and carbon monoxide. If the environment comfort value is out of the standard, the system will give notification if the concentration of harmful gases exceeds the standard, and activates air ventilation and purification devices. With these real-time data, the proposed system can help people make right and timely decisions, and act in time to maintain a healthy environment in the monitored area.",{"EN":1131},"Implementation of an Environmental Quality and Harmful Gases Monitoring System in Cloud",{"VOID":1133},"[\"17707501098773913233\"]",{"VOID":1135},"Yan, Q.-H., Yan, Z.-P., & Tan, C.-W. (2015). Design and implementation of household gas monitoring system based on zigbee+gprs communication. In 2015 International Conference on Intelligent Transportation, Big Data and Smart City, pp. 274–277.\nXu, J., Tang, Y.-N., & Li, X. (2016). High performance combustible gas monitoring system. In 2016 10th International Conference on Sensing Technology (ICST), pp. 1–5.\nJian, F., & Wei, L. (2014). Harmful gases wireless network monitoring system design. In 2014 International Symposium on Computer, Consumer and Control, pp. 551–553.\nLiu, J., & Huang, J. L. X. (2015). Secure sharing of personal health records in cloud computing: Ciphertext policy attribute based signcryption. Future Generation Computer Systems, 52(2015), 67–76.\nSultan, N. (2014). Discovering the potential of cloud computing in accelerating the search for curing serious illnesses. International Journal of Information Management, 34(2014), 221–225.\nBeloglazov, A., & Abawajy, R. B. J. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768.\nMiorandi, D., & Sicari, F. D. P. I. C. S. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516.\nDu, C., & Zhu, S. (2012). Research on urban public safety emergency management early warning system based on technologies for the internet of things. Procedia Engineering, 45(2012), 748–752.\nSun, E., & Zhang, Z. L. X. (2012). The internet of things (IoT) and cloud computing (cc) based tailings dam monitoring and pre-alarm system in mines. Safety Science, 50(4), 811–815.\nSadeghioon, A. M., & Metje, D. C. C. A. S. N. (2014). Research on urban public safety emergency management early warning system based on technologies for the internet of things. Journal of Sensor and Actuator Networks, 3(1), 64–78.\nKim, Y., Suh, J., Cho, J. Y., Singh, S., & Seo, J. S. (2015). Development of real-time pipeline management system for prevention of accidents. International Journal of Control and Automation, 8(1), 211–226.\nLAN\u002FMAN Standards Committee of the IEEE Computer Society, Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE, 2003.\nLönn, J., & Olsson, J. (2005). Zigbee for wireless networking. Master Thesis, Linköping University.\n“ZigBee Specification FAQ”. Zigbee Alliance. Archived from the original on 27 June 2013. Retrieved 14 June 2013.\nYang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2016.1239771.\nMySQL Cluster Architecture Overview. (2004). A mysql® technical white paper. https:\u002F\u002Fconfluence.oceanobservatories.org\u002Fdownload\u002Fattachments\u002F16418744\u002Fmysql-cluster-technical-whitepaper.pdf.\nhttps:\u002F\u002Fdev.mysql.com\u002Fdoc\u002Fmysql-linuxunix-excerpt\u002F5.7\u002Fen\u002Flinux-installation-debian.html.\nHealth Level Seven®INTERNATIONAL. http:\u002F\u002Fwww.hl7.org\u002F.\nCzajkowski, K., Foster, I., & Kesselman, C. (1999). Resource co-allocation in computational grids. In Proceedings of the Eighth IEEE International Symposium on High Performance Distributed Computing (HPDC-8 99).\nFoster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid: En-abling scalable virtual organizations. International Journal of Supercomputer Ap-plications and High Performance Computing, 15(3), 200–222.\nYang, C.-T., Shih, W.-C., Chen, L.-T., Kuo, C.-T., Jiang, F.-C., & Leu, F.-Y. (2015). Accessing medical image file with coallocation HDFS in cloud. Future Generation Computer Systems, 43–44, 61–73.\nYang, C.-T., Shih, W.-C., Huang, C.-L., Jiang, F.-C., & Chu, W. C.-C. (2016). On construction of a distributed data storage system in cloud. Computing, 98(1–2), 93–118.\nASCII (American standard code for information interchange). http:\u002F\u002Fzh.wikipedia.org\u002Fwiki\u002FASCII.\nYang, C.-T., Liao, C.-J., Liu, J.-C., Den, W., Chou, Y.-C., & Tsai, J.-J. (2014). Construction and application of an intelligent air quality monitoring system for healthcare environment. Journal of Medical Systems, 38, 15.\nYang, C.-T., Liu, J.-C., Chen, S.-T., & Lu, H.-W. (2017). Implementation of a big data accessing and processing platform for medical records in cloud. Journal of Medical Systems, 41, 149.",{"VOID":1137},"10.1007\u002Fs40846-018-0383-0","2024-06-26T13:19:01.191+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40846-018-0383-0",[1141,1158,1173,1190,1207],{"id":1142,"sortIndex":21,"researcher":20,"roles":1143,"affiliations":1144,"properties":1153,"displayName":1155,"givenName":20,"familyName":20},"2a621e6a-dff4-4bda-8b1f-1651941176d2",[164],[1145],{"id":1146,"sortIndex":21,"affiliation":1147,"properties":20},"a23a2b46-5fcb-4690-89ee-ec2c6ce7a0c2",{"id":1146,"createTime":20,"updateTime":20,"relativeEntities":1148,"slug":20,"properties":1149,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1152,"statistic":20},[],{"title":1150},{"VI":1151},"Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC",[],{"title":1154,"gsAuthor":1156},{"VI":1155},"Chao-Tung 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nursing overactive bladder syndrome in the lower urinary tract, it is important to understand the origins of urination-desire sensing during natural bladder filling. This study proposes a noninvasive urination-desire sensing method based on bioimpedance (BI) spectrum analysis. For BI measurement of the bladder, two excitation electrodes and two measurement electrodes are placed on the hypogastric surface of the body. The measurement system for BI provided a 0.2-mA alternating current with a 50-kHz frequency, which was sufficient to continuously measure the changes of the bladder during the various control stages of bladder filling. The results show that the higher-frequency spectral power (0.15–0.4 Hz) decreased (p = 0.05) and the low-to-high frequency ratio significantly increased (p = 0.001) during natural bladder filling for 12 healthy male volunteers. In contrast, the change in the lower-frequency spectral power (0.04–0.15 Hz) was insignificant. So to some extent, the method can be used to check the necessity to void for bladder, and assess neural regulation during bladder filling.",{"EN":1284},"Noninvasive Urination-Desire Sensing Method Based on Bladder Bioimpedance Spectrum 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J., Griffiths, D., & de Groat, W. C. (2008). The neural control of micturition. Nature Reviews Neuroscience, 9, 453–466.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":1424},"10.1007\u002Fs10440-022-00541-7",{"id":1420,"text":1426,"url":1422,"identifiers":1427},"Athwal, B. S., Berkley, K. J., Hussain, L., Brennan, A., Craggs, M., Sakakibara, R., et al. (2001). Brain responses to changes in bladder volume and urge to void in healthy men. Brain, 124, 369–377.",{"doi":1424},{"id":1420,"text":1429,"url":1422,"identifiers":1430},"Groat, W. C. (2006). Integrative control of the lower urinary tract: Preclinical perspective. British Journal of Pharmacology, 147, 25–40.",{"doi":1424},{"id":20,"text":1432,"url":20,"identifiers":1433},"Abrams, P., Cardozo, L., Fall, M., Griffiths, D., Ulmsten, P., Kerrebroeck, P., et al. (2002). The standardisation of terminology of lower urinary tract function: Report from the standardisation sub-committee of the international continence society. Neurourology and Urodynamics, 21, 167–178.",{},{"id":1420,"text":1435,"url":1422,"identifiers":1436},"Kershen, R. T., Azazoi, K. M., & Siroky, M. B. (2002). Blood flow, pressure and compliance in the male human bladder. Journal of Urology, 168, 121–125.",{"doi":1424},{"id":1420,"text":1438,"url":1422,"identifiers":1439},"Mehnert, U., Knapp, P. A., Mueller, N., Reitz, A., & Schurch, B. (2009). Heart rate variability: An objective measure of autonomic activity and bladder sensations during urodynamics. Neurourology and Urodynamics, 28, 313–319.",{"doi":1424},{"id":1420,"text":1441,"url":1422,"identifiers":1442},"Weaver, L. C. (1985). Organization of sympathetic responses to distension of urinary bladder. American Journal of Physiology, 248, 236–240.",{"doi":1424},{"id":1420,"text":1444,"url":1422,"identifiers":1445},"Rocha, I., Burnstock, G., & Spyer, K. M. (2001). Effect on urinary bladder function and arterial blood pressure of the activation of putative purine receptors in brainstem areas. Autonomic Neuroscience, 88, 6–15.",{"doi":1424},{"id":20,"text":1447,"url":20,"identifiers":1448},"Habler, H. J., Mclachlan, E. M., Jamieson, J., & Davies, P. J. (1999). Synaptic responses evoked by lower urinary tract stimulation in superior cervical ganglion cells in the rat. Journal of Urology, 161, 1666–1671.",{},{"id":20,"text":1450,"url":20,"identifiers":1451},"Jezemik, S., Wen, J. G., Rijkhoff, N. J., Djurhuus, J. C., & Sinkjaer, T. (2000). Analysis of bladder related nerve cuff electrode recordings from preganglionic pelvic nerve and sacral roots in pigs. Journal of Urology, 163, 1309–1314.",{},{"id":1420,"text":1453,"url":1422,"identifiers":1454},"Huheauk, K., Deffieux, X., Ismael, S. S., Raibaut, P., & Amarenco, G. (2007). Autonomic nervous system activity during bladder filling assessed by heart rate variability analysis in women with idiopathic overactive bladder syndrome or stress urinary incontinence. Journal of Urology, 178, 2483–2487.",{"doi":1424},{"id":1420,"text":1456,"url":1422,"identifiers":1457},"Mccarthy, C. J., Zabbarova, I. V., Brumovsky, P. R., Roppolo, J. R., Gebhart, G. F., & Kanai, A. J. (2009). Spontaneous contractions evoke afferent nerve firing in mouse bladders with detrusor overactivity. Journal of Urology, 181, 1459–1466.",{"doi":1424},{"id":1420,"text":1459,"url":1422,"identifiers":1460},"Waltz, F., & Frederick, M. (1971). Bladder volume sensing by resistance measurement. IEEE Transactions on Biomedical Engineering, 18, 42–46.",{"doi":1424},{"id":1420,"text":1462,"url":1422,"identifiers":1463},"Chitsakul, K., Bouchoucha, M., Lee, J. W., & Cugnenc, P. H. (1991). New method of analysis of epigastric impedance measurement for gastric emptying and motility. Proceedings of IEEE International Conference on Computer-Based Medical System, 4, 10–17.",{"doi":1424},{"id":1420,"text":1465,"url":1422,"identifiers":1466},"Gill, B. C., Fletter, P. C., Zaszczurynski, P. J., Perlin, A., Yachia, D., & Damaser, M. S. (2008). Feasibility of fluid volume conductance to assess bladder volume. Neurourology and Urodynamics, 27, 525–531.",{"doi":1424},{"id":1420,"text":1468,"url":1422,"identifiers":1469},"Shen, L., Valtino, X. A., John, G. W., & Willis, J. T. (1992). The electrode system in impedance-based ventilation measurement. IEEE Transactions on Biomedical Engineering, 39, 1130–1141.",{"doi":1424},{"id":1420,"text":1471,"url":1422,"identifiers":1472},"Ness, T. J., Richter, H., Varner, R., & Fillingim, R. B. (1998). A psychophysical study of discomfort produced by repeated filling of the urinary bladder. Pain, 76, 61–69.",{"doi":1424},{"id":1420,"text":1474,"url":1422,"identifiers":1475},"Wachter, S. D., & Wyndaele, J. J. (2003). Frequency volume charts: A tool to evaluate bladder Sensation. Neurourology and Urodynamics, 22, 638–642.",{"doi":1424},{"id":1477,"text":1478,"url":1479,"identifiers":1480},"48cf1c45-c3f3-47d9-afd3-66593b5729db","Liao, W. C., & Jaw, F. S. (2010). A noninvasive evaluation of autonomic nervous system dysfunction in women with an overactive bladder. International Journal of Gynecology and Obstetrics, 110, 12–17.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS002072921000144X",{"doi":1481},"10.1016\u002Fj.ijgo.2010.03.007",{"id":1483,"text":1484,"url":1485,"identifiers":1486},"7215bcf9-34fe-491a-bfdb-e1760e91787a","Cardoso, A. S., Gonzaga, N. C., Medeiros, C. C., & Carvalho, D. F. (2013). Association of uric acid level with components of metabolic syndrome and non-alcoholic fatty liver disease in overweight or obese children and adolescents. Journal of Pediatrics, 89, 412–418.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS002175571300096X",{"doi":1487},"10.1016\u002Fj.jped.2012.12.008",{"id":1420,"text":1489,"url":1422,"identifiers":1490},"Boyle, J., Bidargaddi, N., Sarela, A., & Mohan, K. (2009). Automatic detection of respiration rate from ambulatory single-lead ECG. IEEE Transactions on Information Technology in Biomedicine, 13, 890–896.",{"doi":1424},{"id":1492,"text":1493,"url":1494,"identifiers":1495},"b117aa3a-4cbf-49b3-bf4f-162505a6aa66","Blok, B. F., & Holstege, G. (1998). The central nervous system control of micturition in cats and humans. Behavioural Brain Research, 92, 119–125.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0166432897001848",{"doi":1496},"10.1016\u002Fs0166-4328(97)00184-8",{"id":20,"text":1498,"url":20,"identifiers":1499},"Kim, C. T., Linsenmeyer, T. A., Kim, H., & Yoon, H. (1998). Bladder volume measurement with electrical impedance analysis in spinal cord-injured patients. American Journal of Physical Medicine and Rehabilitation, 77, 498–502.",{},{"id":1420,"text":1501,"url":1422,"identifiers":1502},"Mudraya, I. S., Revenku, S. V., Nesterov, A. V., Gavrilov, I. Y., & Kirpatovsky, V. I. (2011). Bioimpedance harmonic analysis as a tool to simultaneously assess circulation and nervous control. Physiological Measurement, 32, 959–976.",{"doi":1424},{"id":1420,"text":1504,"url":1422,"identifiers":1505},"Wachter, S. G., Heeinga, R., Koeveringe, G. A., & Gillespie, J. L. (2011). On the nature of bladder sensation: The concept of sensory modulation. Neurourology and Urodynamics, 30, 1220–1226.",{"doi":1424},{"id":1420,"text":1507,"url":1422,"identifiers":1508},"Drinkhill, M. J., Mary, D. A., Ramadan, M. R., & Vacca, G. (1989). The effect of distension of the urinary bladder on activity in efferent renal fibres in anaesthetized dogs. Journal of Physiology, 409, 357–369.",{"doi":1424},{"id":1420,"text":1510,"url":1422,"identifiers":1511},"Robertson, A. S., Griffiths, C. J., Ramsden, P. D., & Neal, D. E. (1994). Bladder function in healthy volunteers: Ambulatory monitoring and conventional urodynamic studies. British Journal of Urology, 73, 242–249.",{"doi":1424},{"id":1420,"text":1513,"url":1422,"identifiers":1514},"Bristow, S. E., & Neal, D. E. (1996). Ambulatory urodynamics. British Journal of Urology, 77, 272–274.",{"doi":1424},{"id":1516,"createTime":1517,"updateTime":1518,"relativeEntities":1519,"slug":1520,"properties":1521,"entityType":153,"verifyStatus":154,"verifyTime":1528,"verifyNote":156,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1529,"fullTextUrl":20,"authors":1530,"publicationType":295,"publisherRelationship":1548,"citationCount":20,"citationInfo":20,"publishDate":1599,"publishYear":1116,"citationAnalyzeStatus":19,"lastCitationAnalyze":1518,"indexDatabases":1600,"openAccess":20,"references":20,"isForceReanalyzing":351},"1d2a1b45-26b4-4e2f-814e-c56144ce58a8","2023-12-29T10:57:32.656+00:00","2026-07-11T19:08:02.414+00:00",[],"Erratum-to-Review-Mechanical-Impedance-and-Its-Relations-to-Motor-Control-Limb-Dynamics-and-Motion-Biomechanics",{"title":1522,"gsPaper":1524,"doi":1526},{"EN":1523},"Erratum to: Review: Mechanical Impedance and Its Relations to Motor Control, Limb Dynamics, and Motion 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classification based upon tissue stiffness is very useful in early diagnosis of cancer; specifically, breast tissue health is diagnosed through tissue stiffness. This paper proposes a method to define the mechanical characteristics of various soft tissues, which could be helpful in discovery of soft tissue abnormalities. This study focused on tissue characterization and the identification of the relationship between tissue properties and pathological mechanics using an elastography technique (method of cancer detection, that uses the response of soft tissue to deformation) based on the Yeoh hyper-elastic model. The suitability of the Yeoh model was validated through compression testing of breast phantoms, animal tissues, and in vivo human tissues. The mean deviation between the known and calculated position was 0.31 ± 0.16 mm. The maximum deviation was less than 0.86 mm. The results indicate that the location of a scintillator within the recording cage imaged with two cameras can be calculated with submillimeter accuracy. We hope that our methods can be applied to improve automatic (even real-time) tracking of various animals in vivo.",{"EN":1611},"Mechanical Characterization of Soft Tissue Constituents for Cancer Detection",{"VOID":1613},"[\"11751139860122171845\"]",{"VOID":1615},"Fischer, A. A. (1987). Pressure algometry over normal muscles. Standard values, validity and reproducibility of pressure threshold. Pain, 30(1), 115–126.\nHayes, W. C., Keer, L. M., Herrmann, G., & Mockros, L. F. (1972). A mathematical analysis for indentation tests of articular cartilage. Journal of Biomechanics, 5(5), 541–551.\nGalea, A. M. (2004). Mapping tactile imaging information: Parameter estimation and deformable registration, Thesis. Harvard University Cambridge, Massachusetts.\nMathis, K. L., et al. (2010). Palpable presentation of breast cancer persists in the era of screening mammography. Journal of the American College of Surgeons, 210(3), 314–318.\n“PP Systems,” SureTouch.\nKrouskop, T. A., Wheeler, T. M., Kallel, F., Garra, B. S., & Hall, T. (1998). Elastic moduli of breast and prostate tissues under compression. Ultrasonic Imaging, 20(4), 260–274.\nO’Hagan, J. J., & Samani, A. (2008). Measurement of the hyperelastic properties of tissue slices with tumour inclusion. Physics in Medicine & Biology, 53(24), 7087–7106.\nWellman, P., Howe, R. D., Dalton, E., & Kern, K. A. (1999). Breast tissue stiffness in compression is correlated to histological diagnosis. Harvard BioRobotics Laboratory Technical Report, pp. 1–15.\nNover, A. B., et al. (2009). Modern breast cancer detection: A technological review. International Journal of Biomedical Imaging, 2009, 902326.\nHaeri, Z., Shokoufi, M., Jenab, M., Janzen, R., & Golnaraghi, F. (2016). Electrical impedance spectroscopy for breast cancer diagnosis: Clinical study. Integrative Cancer Science and Therapeutics, 3(6), 1–6.\nOphir, J., Cespedes, I., Ponnekanti, H., Yazdi, I., & Li, X. (1991). 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