{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:58:29Z","timestamp":1769911109902,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-AG050672"],"award-info":[{"award-number":["R01-AG050672"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (&gt;64%) and NLOB (&gt;90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.<\/jats:p>","DOI":"10.3390\/s22093334","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T22:20:20Z","timestamp":1651098020000},"page":"3334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8006-8299","authenticated-orcid":false,"given":"Shuaijie","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL 60612, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8612-5805","authenticated-orcid":false,"given":"Fabio","family":"Miranda","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA"}]},{"given":"Yiru","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL 60612, USA"}]},{"given":"Rahiya","family":"Rasheed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA"}]},{"given":"Tanvi","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL 60612, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"993","DOI":"10.15585\/mmwr.mm6537a2","article-title":"Falls and fall injuries among adults aged \u2265 65 years\u2014United States, 2014","volume":"65","author":"Bergen","year":"2016","journal-title":"Morb. Mortal. Wkly. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/ageing\/26.4.261","article-title":"Circumstances and consequences of falls in independent community-dwelling older adults","volume":"26","author":"Berg","year":"1997","journal-title":"Age Ageing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.humov.2015.12.007","article-title":"Falls in young adults: Perceived causes and environmental factors assessed with a daily online survey","volume":"46","author":"Heijnen","year":"2016","journal-title":"Hum. Mov. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1177\/1460408612463145","article-title":"The impact of falls in the elderly","volume":"15","author":"Hartholt","year":"2013","journal-title":"Trauma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.anl.2013.05.002","article-title":"Risk factors of falls in community dwelling active elderly","volume":"41","author":"Tuunainen","year":"2014","journal-title":"Auris Nasus Larynx"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1111\/j.1532-5415.1997.tb05975.x","article-title":"Circumstances of falls resulting in hip fractures among older people","volume":"45","author":"Norton","year":"1997","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1056\/NEJM198812293192604","article-title":"Risk-Factors for Falls among Elderly Persons Living in the Community","volume":"319","author":"Tinetti","year":"1988","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1136\/ip.2004.005835","article-title":"Gender differences for non-fatal unintentional fall related injuries among older adults","volume":"11","author":"Stevens","year":"2005","journal-title":"Inj. Prev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1159\/000067948","article-title":"Evidence-based guidelines for the secondary prevention of falls in older adults","volume":"49","author":"Moreland","year":"2003","journal-title":"Gerontology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1007\/s00391-013-0559-8","article-title":"Fall detection with body-worn sensors","volume":"46","author":"Schwickert","year":"2013","journal-title":"Z. Gerontol. Geriatr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103946","DOI":"10.1016\/j.ijmedinf.2019.08.006","article-title":"Fall detection and fall risk assessment in older person using wearable sensors: A systematic review","volume":"130","author":"Bet","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1519\/JPT.0000000000000181","article-title":"Detection of near falls using wearable devices: A systematic review","volume":"42","author":"Pang","year":"2019","journal-title":"J. Geriatr. Phys. Ther."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Broadley, R.W., Klenk, J., Thies, S.B., Kenney, L.P., and Granat, M.H. (2018). Methods for the real-world evaluation of fall detection technology: A scoping review. Sensors, 18.","DOI":"10.3390\/s18072060"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"271","DOI":"10.3109\/17483107.2014.961179","article-title":"Literature review on monitoring technologies and their outcomes in independently living elderly people","volume":"10","author":"Peetoom","year":"2015","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/JBHI.2017.2677901","article-title":"Differences between gait on stairs and flat surfaces in relation to fall risk and future falls","volume":"21","author":"Wang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ponti, M., Bet, P., Oliveira, C.L., and Castro, P.C. (2017). Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175559"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/TBME.2009.2033038","article-title":"Longitudinal falls-risk estimation using triaxial accelerometry","volume":"57","author":"Narayanan","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Howcroft, J., Lemaire, E.D., Kofman, J., and McIlroy, W.E. (2018). Dual-task elderly gait of prospective fallers and non-fallers: A wearable-sensor based analysis. Sensors, 18.","DOI":"10.3390\/s18041275"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.gaitpost.2017.03.037","article-title":"Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study","volume":"55","author":"Hausdorff","year":"2017","journal-title":"Gait Posture"},{"key":"ref_20","first-page":"A13","article-title":"Peer reviewed: A catalog of biases in questionnaires","volume":"2","author":"Choi","year":"2005","journal-title":"Prev. Chronic Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"M761","DOI":"10.1093\/gerona\/56.12.M761","article-title":"Fall risk assessment measures: An analytic review","volume":"56","author":"Perell","year":"2001","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1186\/s12984-021-00918-z","article-title":"A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls","volume":"18","author":"Harari","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D\u2019Ambruoso, D., Volpe, A., Loconsole, C., and Stroppa, F. (2014, January 23\u201325). Fall detection in indoor environment with kinect sensor. Proceedings of the 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Alberobello, Italy.","DOI":"10.1109\/INISTA.2014.6873638"},{"key":"ref_24","first-page":"625","article-title":"Characteristics of falls in 70 year olds in Jerusalem","volume":"32","author":"Ginsberg","year":"1996","journal-title":"Isr. J. Med. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1023\/A:1007531101765","article-title":"Differential risk factor profiles for indoor and outdoor falls in older people living at home in Nottingham, UK","volume":"15","author":"Bath","year":"1999","journal-title":"Eur. J. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.gaitpost.2011.11.016","article-title":"Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects","volume":"35","author":"Kangas","year":"2012","journal-title":"Gait Posture"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bagala, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J.M., Zijlstra, W., and Klenk, J. (2012). Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037062"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1519\/JPT.0b013e3182abe779","article-title":"Fall detection devices and their use with older adults: A systematic review","volume":"37","author":"Chaudhuri","year":"2014","journal-title":"J. Geriatr. Phys. Ther."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hemmatpour, M., Ferrero, R., Montrucchio, B., and Rebaudengo, M. (2016, January 15\u201316). A baseline walking dataset exploiting accelerometer and gyroscope for fall prediction and prevention systems. Proceedings of the 11th EAI International Conference on Body Area Networks, Turin, Italy.","DOI":"10.4108\/eai.15-12-2016.2267646"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1007\/s40279-015-0413-9","article-title":"Predictive and reactive locomotor adaptability in healthy elderly: A systematic review and meta-analysis","volume":"45","author":"Bohm","year":"2015","journal-title":"Sports Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Arnold, C.M., and Faulkner, R.A. (2007). The history of falls and the association of the timed up and go test to falls and near-falls in older adults with hip osteoarthritis. BMC Geriatr., 7.","DOI":"10.1186\/1471-2318-7-17"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3928\/0098-9134-19931201-06","article-title":"Near falls incidence: A study of older adults in the community","volume":"19","author":"Ryan","year":"1993","journal-title":"J. Gerontol. Nurs."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1136\/jnnp.2005.066258","article-title":"Falls and stumbles in myotonic dystrophy","volume":"77","author":"Wiles","year":"2006","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.apmr.2008.11.007","article-title":"Self-report of missteps in older adults: A valid proxy of fall risk?","volume":"90","author":"Srygley","year":"2009","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1111\/j.1532-5415.1990.tb03455.x","article-title":"Multiple Stumbles: A Risk Factor for Falls in Community-Dwelling Elderly; A Prospective Study","volume":"38","author":"Teno","year":"1990","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/ggi.12112","article-title":"Association between site-specific muscle loss of lower body and one-leg standing balance in active women: The HIREGASAKI study","volume":"14","author":"Abe","year":"2014","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Karel, J.M., Senden, R., Janssen, J.E., Savelberg, H., Grimm, B., Heyligers, I., Peeters, R., and Meijer, K. (September, January 31). Towards unobtrusive in vivo monitoring of patients prone to falling. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5626232"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Weiss, A., Shimkin, I., Giladi, N., and Hausdorff, J.M. (2010). Automated detection of near falls: Algorithm development and preliminary results. BMC Res. Notes, 3.","DOI":"10.1186\/1756-0500-3-62"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.clinbiomech.2009.09.002","article-title":"A three-dimensional kinematic and kinetic comparison of overground and treadmill walking in healthy elderly subjects","volume":"25","author":"Watt","year":"2010","journal-title":"Clin. Biomech."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s001980070086","article-title":"Fracture risk associated with a fall according to type of fall among the elderly","volume":"11","author":"Luukinen","year":"2000","journal-title":"Osteoporos. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: Principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","article-title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","volume":"55","author":"Aziz","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.measurement.2019.03.079","article-title":"Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch","volume":"140","author":"Chen","year":"2019","journal-title":"Measurement"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.07.017","article-title":"A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox","volume":"111","author":"Jing","year":"2017","journal-title":"Measurement"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","article-title":"Machine learning and deep learning methods for cybersecurity","volume":"6","author":"Xin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"\u201cMini-mental state\u201d. A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1385\/JCD:1:3:219","article-title":"Quantitative ultrasound (QUS) of the heel predicts wrist and osteoporosis-related fractures in women age 45-75 years","volume":"1","author":"Thompson","year":"1998","journal-title":"J. Clin. Densitom. Off. J. Int. Soc. Clin. Densitom."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1136\/bjsports-2015-095452","article-title":"Step training improves reaction time, gait and balance and reduces falls in older people: A systematic review and meta-analysis","volume":"51","author":"Okubo","year":"2017","journal-title":"Br. J. Sport Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/S1067-2516(00)80061-7","article-title":"The durability of the Semmes-Weinstein 5.07 monofilament","volume":"39","author":"Yong","year":"2000","journal-title":"J. Foot Ankle Surg."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1007\/s11357-014-9640-5","article-title":"Learning from laboratory-induced falling: Long-term motor retention among older adults","volume":"36","author":"Pai","year":"2014","journal-title":"Age"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00221-005-0189-5","article-title":"Adaptive control of gait stability in reducing slip-related backward loss of balance","volume":"170","author":"Bhatt","year":"2006","journal-title":"Exp. Brain Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1088\/1361-6579\/aae0eb","article-title":"Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope","volume":"39","author":"Wang","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait Posture"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","article-title":"A time series forest for classification and feature extraction","volume":"239","author":"Deng","year":"2013","journal-title":"Inf. Sci"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s10618-019-00633-3","article-title":"Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations","volume":"33","author":"Nguyen","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_56","unstructured":"Le Guennec, A., Malinowski, S., and Tavenard, R. (2016, January 19\u201323). Data augmentation for time series classification using convolutional neural networks. Proceedings of the ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data 2016, Riva del Garda, Italy."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"InceptionTime: Finding AlexNet for time series classification","volume":"34","author":"Fawaz","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.apmr.2009.10.032","article-title":"Inoculation Against Falls: Rapid Adaptation by Young and Older Adults to Slips During Daily Activities","volume":"91","author":"Pai","year":"2010","journal-title":"Arch. Phys. Med. Rehab."},{"key":"ref_59","unstructured":"He, H., Bai, Y., Panagiotakos, D., Garcia, E., and Li, S.A. (2008, January 1\u20138). Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/S0021-9290(00)00037-3","article-title":"Foot displacement but not velocity predicts the outcome of a slip induced in young subjects while walking","volume":"33","author":"Brady","year":"2000","journal-title":"J. Biomech."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.gaitpost.2008.02.008","article-title":"Modifiable performance domain risk-factors associated with slip-related falls","volume":"28","author":"Troy","year":"2008","journal-title":"Gait Posture"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1007\/s10439-020-02482-4","article-title":"Is There an Optimal Recovery Step Landing Zone Against Slip-Induced Backward Falls During Walking?","volume":"48","author":"Wang","year":"2020","journal-title":"Ann. Biomed. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1016\/j.jbiomech.2009.05.009","article-title":"Role of stability and limb support in recovery against a fall following a novel slip induced in different daily activities","volume":"42","author":"Yang","year":"2009","journal-title":"J. Biomech."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.jelekin.2018.09.005","article-title":"Compensatory strategy between trunk-hip kinematics and reaction time following slip perturbation between subjects with and without chronic low back pain","volume":"43","author":"McDowel","year":"2018","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1080\/00140130802567087","article-title":"Effects of lower extremity muscle fatigue on the outcomes of slip-induced falls","volume":"51","author":"Parijat","year":"2008","journal-title":"Ergonomics"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/j.1532-5415.2007.01148.x","article-title":"Interventions to reduce fear of falling in community-living older people: A systematic review","volume":"55","author":"Zijlstra","year":"2007","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"S331","DOI":"10.1249\/00005768-200605001-02293","article-title":"The Effects of Square Stepping Exercise vs. Strength and Balance Training on Fall Risk Factors","volume":"38","author":"Shigematsu","year":"2006","journal-title":"Med. Sci. Sport Exer."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ergon.2005.08.005","article-title":"Nonfatal occupational injuries associated with slips and falls in the United States","volume":"36","author":"Yoon","year":"2006","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/JSEN.2014.2357035","article-title":"Power-Efficient Interrupt-Driven Algorithms for Fall Detection and Classification of Activities of Daily Living","volume":"15","author":"Yuan","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"38670","DOI":"10.1109\/ACCESS.2019.2906693","article-title":"A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_71","unstructured":"(2019). FRAIL\u2014Frail Assessment in Daily Living Detection of Fall Events and Monitoring of physical Activity of elderly People in frail or pre-frail Health Condition. Z. Gerontol. Geriatr., 52, S155."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.1007\/s00521-022-06886-2","article-title":"Data portability for activities of daily living and fall detection in different environments using radar micro-doppler","volume":"34","author":"Shah","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"15462","DOI":"10.1038\/s41598-021-94699-z","article-title":"Muscle synergy differences between voluntary and reactive backward stepping","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/TASE.2018.2884723","article-title":"Inertial sensor-based slip detection in human walking","volume":"16","author":"Trkov","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Trkov, M., Chen, K., Yi, J., and Liu, T. (2015, January 7\u201311). Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs). Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, Korea.","DOI":"10.1109\/AIM.2015.7222645"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/JBHI.2020.3025049","article-title":"Wearables and deep learning classify fall risk from gait in multiple sclerosis","volume":"25","author":"Meyer","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nait Aicha, A., Englebienne, G., Van Schooten, K.S., Pijnappels, M., and Kr\u00f6se, B. (2018). Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors, 18.","DOI":"10.3390\/s18051654"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Palmerini, L., Klenk, J., Becker, C., and Chiari, L. (2020). Accelerometer-based fall detection using machine learning: Training and testing on real-world falls. Sensors, 20.","DOI":"10.3390\/s20226479"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hussain, F., Umair, M.B., Ehatisham-ul-Haq, M., Pires, I.M., Valente, T., Garcia, N.M., and Pombo, N. (2019). An efficient machine learning-based elderly fall detection algorithm. arXiv.","DOI":"10.21203\/rs.3.rs-39065\/v1"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s002210050327","article-title":"Control of reactive balance adjustments in perturbed human walking: Roles of proximal and distal postural muscle activity","volume":"119","author":"Tang","year":"1998","journal-title":"Exp. Brain Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1152\/jn.01226.2006","article-title":"Proactive and reactive mechanisms play a role in stepping on inverting surfaces during gait","volume":"98","author":"Nieuwenhuijzen","year":"2007","journal-title":"J. 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