Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions

W. H. Organization, 2008 Hawley-Hague, 2014, Older adults’ perceptions of technologies aimed at falls prevention, detection or monitoring: a systematic review, Int. J. Med. Inform., 83, 416, 10.1016/j.ijmedinf.2014.03.002 Painter, 2012, Fear of falling and its relationship with anxiety, depression, and activity engagement among community-dwelling older adults, Am. J. Occup. Ther., 66, 169, 10.5014/ajot.2012.002535 Kannus, 2005, Prevention of falls and consequent injuries in elderly people, Lancet, 366, 1885, 10.1016/S0140-6736(05)67604-0 Kronfol, 2012, Biological, medical and behavioral risk factors on falls, World Health Organ. Carter, 1997, Environmental hazards in the homes of older people, Age Ageing, 26, 195, 10.1093/ageing/26.3.195 Gill, 1999, A population-based study of environmental hazards in the homes of older persons, Am. J. Public Health, 89, 553, 10.2105/AJPH.89.4.553 Podsiadlo, 1991, The timed” up & go”: a test of basic functional mobility for frail elderly persons, J. Am. Geriatr. Soc., 39, 142, 10.1111/j.1532-5415.1991.tb01616.x Shumway-Cook, 2000, Predicting the probability for falls in community-dwelling older adults using the timed up & go test, Phys. Ther., 80, 896, 10.1093/ptj/80.9.896 Tinetti, 1986, Performance-oriented assessment of mobility problems, J. Am. Geriatr. Soc., 34, 119, 10.1111/j.1532-5415.1986.tb05480.x Schoene, 2013, Discriminative ability and predictive validity of the timed Up and Go test in identifying older people who fall: systematic review and meta-analysis, J. Am. Geriatr. Soc., 61, 202, 10.1111/jgs.12106 Hamel, 2005, Foot clearance during stair descent: effects of age and illumination, Gait Posture, 21, 135, 10.1016/j.gaitpost.2004.01.006 Robles-García, 2015, Spatiotemporal gait patterns during overt and covert evaluation in patients with Parkinson’s disease and healthy subjects: Is there a Hawthorne effect?, J. Appl. Biomech., 31, 189, 10.1123/jab.2013-0319 Del Din, 2016, Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length, J. Neuroeng. Rehabil., 13, 10.1186/s12984-016-0154-5 Hillel, 2019, Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring, Eur. Rev. Aging Phys. Act., 16, 1, 10.1186/s11556-019-0214-5 Rispens, 2016, Fall-related gait characteristics on the treadmill and in daily life, J. Neuroeng. Rehabil., 13, 10.1186/s12984-016-0118-9 Takayanagi, 2019, Relationship between daily and in-laboratory gait speed among healthy community-dwelling older adults, Sci. Rep., 9, 2, 10.1038/s41598-019-39695-0 Diraco, 2017, A radar-based smart sensor for unobtrusive elderly monitoring in ambient assisted living applications, Biosensors, 7, 10.3390/bios7040055 Kaye, 2012, One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults, Gait Posture, 35, 10.1016/j.gaitpost.2011.09.006 Gabel, 2012, Full body gait analysis with kinect, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2012 Cippitelli, 2015, Kinect as a tool for gait analysis: validation of a real-time joint extraction algorithm working in side view, Sensors, 15, 1417, 10.3390/s150101417 Phillips, 2017, Using embedded sensors in independent living to predict gait changes and falls, West. J. Nurs. Res., 39, 78, 10.1177/0193945916662027 Auvinet, 2017, Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using microsoft kinect data, Gait Posture, 51, 162, 10.1016/j.gaitpost.2016.08.022 Stone, 2013, Unobtrusive, continuous, in-home gait measurement using the microsoft kinect, IEEE Trans. Biomed. Eng., 60, 10.1109/TBME.2013.2266341 Di Rosa, 2017, Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: a pilot study, Gait Posture, 55, 6, 10.1016/j.gaitpost.2017.03.037 Moufawad El Achkar, 2016, Physical behavior in older persons during daily life: insights from instrumented shoes, Sensors (Switzerland), 16, 10.3390/s16081225 el Achkar, 2018, Classification and characterization of postural transitions using instrumented shoes, Med. Biol. Eng. Comput., 56, 1403, 10.1007/s11517-017-1778-8 Nouredanesh, 2019, 176 Kim, 2015, A wearable smartphone-enabled camera-based system for gait assessment, Gait Posture, 42, 138, 10.1016/j.gaitpost.2015.05.001 Iluz, 2015, Can a body-fixed sensor reduce Heisenberg’s uncertainty when it comes to the evaluation of mobility? Effects of aging and fall risk on transitions in daily living, J. Gerontol. Ser. A Biomed. Sci. Med. Sci., 71, 1459, 10.1093/gerona/glv049 Rispens, 2015, Do extreme values of daily-life gait characteristics provide more information about fall risk than median values?, JMIR Res. Protoc., 4, 10.2196/resprot.3931 Weiss, 2013, Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings, Neurorehabil. Neural Repair, 27, 742, 10.1177/1545968313491004 Ihlen, 2015, The discriminant value of phase-dependent local dynamic stability of daily life walking in older adult Community-dwelling fallers and nonfallers, Biomed Res. Int., 2015, 10.1155/2015/402596 Ihlen, 2016, The complexity of daily life walking in older adult community-dwelling fallers and non-fallers, J. Biomech., 49 Ihlen, 2016, A comparison study of local dynamic stability measures of daily life walking in older adult community-dwelling fallers and non-fallers, J. Biomech., 49 Weiss, 2014, Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days, PLoS One, 9, 10.1371/journal.pone.0096675 Iluz, 2014, Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting, J. Neuroeng. Rehabil., 11, 10.1186/1743-0003-11-48 Van Schooten, 2016, Daily-life gait quality as predictor of falls in older people: a 1-year prospective cohort study, PLoS One, 11, 10.1371/journal.pone.0158623 Nait Aicha, 2018, Deep learning to predict falls in older adults based on daily-life trunk accelerometry, Sensors, 18, 10.3390/s18051654 Ihlen, 2018, Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking, Front. Aging Neurosci., 10, 10.3389/fnagi.2018.00044 Van Schooten, 2015, Assessing physical activity in older adults: required days of trunk accelerometer measurements for reliable estimation, J. Aging Phys. Act., 23, 9, 10.1123/JAPA.2013-0103 van Schooten, 2015, Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults, J. Gerontol. A. Biol. Sci. Med. Sci., 70, 608, 10.1093/gerona/glu225 Mactier, 2015, The relationship between real world ambulatory activity and falls in incident parkinson’s disease: influence of classification scheme, Parkinsonism Relat. Disord., 21, 236, 10.1016/j.parkreldis.2014.12.014 Hiorth, 2016, Impact of falls on physical activity in people with Parkinson’s disease, J. Parkinsons. Dis., 6, 175, 10.3233/JPD-150640 Leach, 2018, Natural turn measures predict recurrent falls in community-dwelling older adults: a longitudinal cohort study, Sci. Rep., 8, 10.1038/s41598-018-22492-6 Del, 2017, Analysis of free-living gait in older adults with and without Parkinson’s disease and with and without a history of falls: identifying generic and disease specific characteristics, J. Gerontol. Ser. A Med. Sci. Rispens, 2015, Identification of fall risk predictors in daily life measurements: gait characteristics’ reliability and association with self-reported fall history, Neurorehabil. Neural Repair, 29, 54, 10.1177/1545968314532031 Brodie, 2015, 62, 2588 Brodie, 2017, Comparison between clinical gait and daily-life gait assessments of fall risk in older people, Geriatr. Gerontol. Int., 17, 2274, 10.1111/ggi.12979 Mohler, 2016, Motor performance and physical activity as predictors of prospective falls in community-dwelling older adults by frailty level: application of wearable technology, Gerontology, 62, 10.1159/000445889 Schwenk, 2014, Sensor-derived physical activity parameters can predict future falls in people with dementia, Gerontology, 60, 10.1159/000363136 Pozaic, 2016, Sit-to-stand transition reveals acute fall risk in activities of daily living, IEEE J. Transl. Eng. Heal. Med., 4 Mancini, 2016, Continuous monitoring of turning mobility and its association to falls and cognitive function: a pilot study, J. Gerontol. Ser. A Biomed. Sci. Med. Sci., 71, 1102, 10.1093/gerona/glw019 Gietzelt, 2014, A prospective field study for sensor-based identification of fall risk in older people with dementia, Inf. Health Soc. Care, 39 Kantz, 2004, 7 Tobola, 2015, Sampling rate impact on energy consumption of biomedical signal processing systems, 2015 IEEE 12th Int. Conf. Wearable Implant. Body Sens. Networks, BSN 2015, 10.1109/BSN.2015.7299392 Rispens, 2014, Consistency of gait characteristics as determined from acceleration data collected at different trunk locations, Gait Posture, 40, 187, 10.1016/j.gaitpost.2014.03.182 Montesinos, 2018, Wearable inertial sensors for fall risk assessment and prediction in older adults: a systematic review and meta-analysis, IEEE Trans. Neural Syst. Rehabil. Eng., 26, 573, 10.1109/TNSRE.2017.2771383 Godfrey, 2017, Wearables for independent living in older adults: gait and falls, Maturitas, 100, 16, 10.1016/j.maturitas.2017.03.317 Barry, 2015, Defining ambulatory bouts in free-living activity: impact of brief stationary periods on bout metrics, Gait Posture, 42, 594, 10.1016/j.gaitpost.2015.07.062 Nouredanesh, 2019, IMU, sEMG, or their cross-correlation and temporal similarities: which features detects lateral compensatory balance reactions more accurately?, Comput. Methods Programs Biomed., 10.1016/j.cmpb.2019.105003 De Venuto, 2015, Combining EEG and EMG signals in a wireless system for preventing fall in neurodegenerative diseases, 317 Castaldo, 2017, Fall prediction in hypertensive patients via short-term HRV analysis, IEEE J. Biomed. Health Informatics, 21, 399, 10.1109/JBHI.2016.2543960 el Achkar, 2016, Instrumented shoes for activity classification in the elderly, Gait Posture, 44, 12, 10.1016/j.gaitpost.2015.10.016 Ordóñez, 2016, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors (Switzerland), 16, 10.3390/s16010115 Twardzik, 2019, What features of the built environment matter most for mobility? Using wearable sensors to capture real-time outdoor environment demand on gait performance, Gait Posture, 68, 437, 10.1016/j.gaitpost.2018.12.028 Ojeda, 2019, Reconstruction of body motion during self-reported losses of balance in community-dwelling older adults, Med. Eng. Phys., 64, 86, 10.1016/j.medengphy.2018.12.008 Hickey, 2016, Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis, Physiol. Meas., 38, 10.1088/1361-6579/38/1/N1 Taylor, 2015, Context focused older adult mobility and gait assessment, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Nouredanesh, 2016, Wearable vision detection of environmental fall risks using convolutional neural networks, arXiv Prepr. arXiv1611.00684 Nouredanesh, 2016, Wearable vision detection of environmental fall risks using convolutional neural networks, arXiv Prepr. arXiv1611.00684 Lord, 2012, Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach, J. Gerontol. Ser. A Biomed. Sci. Med. Sci., 68, 820, 10.1093/gerona/gls255 Lord, 2013, 28, 1534