{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T05:06:28Z","timestamp":1768626388334,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,11,20]],"date-time":"2015-11-20T00:00:00Z","timestamp":1447977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.<\/jats:p>","DOI":"10.3390\/s151129393","type":"journal-article","created":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T01:57:02Z","timestamp":1448330222000},"page":"29393-29407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms"],"prefix":"10.3390","volume":"15","author":[{"given":"Shengyun","family":"Liang","sequence":"first","affiliation":[{"name":"Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China"},{"name":"College of mathematics and statistics, Shenzhen University, Shenzhen 518055, China"}]},{"given":"Yunkun","family":"Ning","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China"}]},{"given":"Huiqi","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7033-9806","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China"}]},{"given":"Zhanyong","family":"Mei","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology, No.1, Third East Road, Erxianqiao, Chengdu 610059, China"}]},{"given":"Yingnan","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Research Center of Urban System Engineering, Beijing 100035, China"}]},{"given":"Guoru","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2015,11,20]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) Falls. Available online: http:\/\/www.who.int\/mediacentre\/factsheets\/fs344\/en \/index.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1056\/NEJM199708283370907","article-title":"Injury prevention. Second of two parts","volume":"337","author":"Rivara","year":"1997","journal-title":"N. Engl. J. Med."},{"key":"ref_3","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. A Biol. Sci. Med. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1093\/ageing\/afl165","article-title":"Multifactorial and functional mobility assessment tools for fall risk among older adults in community, home-support, long-term and acute care settings","volume":"36","author":"Scott","year":"2007","journal-title":"Age Ageing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1093\/ptj\/80.9.896","article-title":"Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test","volume":"80","author":"Brauer","year":"2000","journal-title":"Phys. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3233\/VES-2000-10205","article-title":"The dynamic gait index relates to self-reported fall history in individuals with vestibular dysfunction","volume":"10","author":"Whitney","year":"2000","journal-title":"J. Vestib. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3138\/ptc.41.5.240","article-title":"Balance and its measure in the elderly: A review","volume":"41","author":"Berg","year":"1989","journal-title":"Physiother. Can."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/0002-9343(86)90717-5","article-title":"Fall risk index for elderly patients based on number of chronic disabilities","volume":"80","author":"Tinetti","year":"1986","journal-title":"Am. J. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/1471-2318-14-14","article-title":"Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: A systematic review and meta-analysis","volume":"14","author":"Barry","year":"2014","journal-title":"BMC Geriatr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9884","DOI":"10.3390\/s120709884","article-title":"Foot plantar pressure measurement system: A review","volume":"12","author":"Zayegh","year":"2012","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000089820","article-title":"Force platform measurements as predictors of falls among older people\u2014A review","volume":"52","author":"Piirtola","year":"2006","journal-title":"Gerontology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.gaitpost.2008.05.015","article-title":"Application of principal component analysis in vertical ground reaction force to discriminate normal and abnormal gait","volume":"29","author":"Muniz","year":"2009","journal-title":"Gait Posture"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/TSMCB.2008.927722","article-title":"Subject recognition based on ground reaction force measurements of gait signals","volume":"38","author":"Moustakidis","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_14","first-page":"55","article-title":"Control of a legged locomotor device using a ground force sensor for fall prevention","volume":"109","author":"Kitamura","year":"2010","journal-title":"IEICE Tech. Rep. ME Biol. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.archger.2007.10.006","article-title":"Relationships between ground reaction force parameters during a sit-to-stand movement and physical activity and falling risk of the elderly and a comparison of the movement characteristics between the young and the elderly","volume":"48","author":"Yamada","year":"2009","journal-title":"Arch. Gerontol. Geriat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/1475-925X-12-101","article-title":"Sample entropy characteristics of movement for four foot types based on plantar centre of pressure during stance phase","volume":"12","author":"Mei","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13123","DOI":"10.1063\/1.3081406","article-title":"Influence of noise on the sample entropy algorithm","volume":"19","author":"Ramdani","year":"2009","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aboy, M., Cuesta-Frau, D., Austin, D., and Mico-Tormos, P. (2007, January 22\u201326). Characterization of sample entropy in the context of biomedical signal analysis. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353701"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting falls with wearable sensors using machine learning techniques","volume":"14","author":"Barshan","year":"2014","journal-title":"Sensors"},{"key":"ref_22","unstructured":"Langley, P., and Iba, W. (September, January 28). Average-case analysis of a nearest neighbor algorithm. Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-08-047865-4.50007-7"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.patrec.2005.12.016","article-title":"A local mean-based nonparametric classifier","volume":"27","author":"Mitani","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1016\/j.eswa.2008.02.003","article-title":"Pseudo nearest neighbor rule for pattern classification","volume":"36","author":"Zeng","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.knosys.2014.07.020","article-title":"Improved pseudo nearest neighbor classification","volume":"70","author":"Gou","year":"2014","journal-title":"Knowl.-Based. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1016\/S0031-3203(03)00136-5","article-title":"Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers","volume":"36","author":"Cawley","year":"2003","journal-title":"Pattern Recogn."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, S., Yang, Y., and Liu, W. (2011, January 19\u201320). An enhanced fall detection approach based on cost sensitivity analysis. Proceedings of the IEEE ACIS International Symposium on Software and Network Engineering, Seoul, Korea.","DOI":"10.1109\/SSNE.2011.30"},{"key":"ref_29","unstructured":"StatisticsSolutions, Correlation (Pearson, Kendall, Spearman). Available online: http:\/\/www.statisticssolutions.com\/correlation-pearson-kendall-spearman\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1080\/10255842.2011.627329","article-title":"Automatic individual calibration in fall detection\u2014An integrative ambulatory measurement framework","volume":"16","author":"Liu","year":"2013","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1111\/j.1748-1716.1989.tb08655.x","article-title":"Ground reaction forces at different speeds of human walking and running","volume":"136","author":"Nilsson","year":"1989","journal-title":"Acta. Physiol. Scand."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20392","DOI":"10.3390\/s150820392","article-title":"Identification of foot pathologies based on plantar pressure asymmetry","volume":"15","author":"Wafai","year":"2015","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1093\/ptj\/80.12.1188","article-title":"Contributions of lower-limb muscle power in gait of people without impairments","volume":"80","author":"Sadeghi","year":"2000","journal-title":"Phys. Ther."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0268-0033(95)00060-7","article-title":"Sit to stand from progressively lower seat heights\u2014Alterations in angular velocity","volume":"11","author":"Schenkman","year":"1996","journal-title":"Clin. Biomech. (Bristol Avon)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"M539","DOI":"10.1093\/gerona\/57.8.M539","article-title":"Sit-to-stand performance depends on sensation, speed, balance, and psychological status in addition to strength in older people","volume":"57","author":"Lord","year":"2002","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.gaitpost.2014.05.064","article-title":"Can sit-to-stand lower limb muscle power predict fall status?","volume":"40","author":"Cheng","year":"2014","journal-title":"Gait Posture"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1177\/0898264313475813","article-title":"Performance on five times sit-to-stand task as a predictor of subsequent falls and disability in older persons","volume":"25","author":"Zhang","year":"2013","journal-title":"J. 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