Random forest algorithms to classify frailty and falling history in seniors using plantar pressure measurement insoles: a large-scale feasibility study

BMC Geriatrics

Research

Julien Tripette¹  Dian Ren²  Yuji Ohta³  Leo Cazenille⁴  Nathanael Aubert-Kato⁵  Emi Anzai⁶ 

[1] Center for Interdisciplinary AI and Data Science, Ochanomizu University, Tokyo, Japan;Department of Human-Environmental Science, Faculty of Human Life and Environmental Sciences, Ochanomizu University, Tokyo, Japan;Department of Cooperative Major in Human Centered Engineering, Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan;Department of Human-Environmental Science, Faculty of Human Life and Environmental Sciences, Ochanomizu University, Tokyo, Japan;Faculty of Core Research Natural Science Division, Ochanomizu University, Tokyo, Japan;Department of Information Sciences, Ochanomizu University, Tokyo, Japan;Department of Information Sciences, Ochanomizu University, Tokyo, Japan;Center for Interdisciplinary AI and Data Science, Ochanomizu University, Tokyo, Japan;Faculty of Engineering, Nara Women’s University, Nara, Japan;

关键词: Frailty;    Fall risk;    Aging;    Plantar pressure;    Smart-insole;    Balance;    Walking;    Gait analysis;    Random forest classifier;   

DOI  :  10.1186/s12877-022-03425-5

 received in 2022-04-08, accepted in 2022-08-30,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundFrailty and falls are two adverse characteristics of aging that impair the quality of life of senior people and increase the burden on the healthcare system. Various methods exist to evaluate frailty, but none of them are considered the gold standard. Technological methods have also been proposed to assess the risk of falling in seniors. This study aims to propose an objective method for complementing existing methods used to identify the frail state and risk of falling in older adults.MethodA total of 712 subjects (age: 71.3 ± 8.2 years, including 505 women and 207 men) were recruited from two Japanese cities. Two hundred and three people were classified as frail according to the Kihon Checklist. One hundred and forty-two people presented with a history of falling during the previous 12 months. The subjects performed a 45 s standing balance test and a 20 m round walking trial. The plantar pressure data were collected using a 7-sensor insole. One hundred and eighty-four data features were extracted. Automatic learning random forest algorithms were used to build the frailty and faller classifiers. The discrimination capabilities of the features in the classification models were explored.ResultsThe overall balanced accuracy for the recognition of frail subjects was 0.75 ± 0.04 (F1-score: 0.77 ± 0.03). One sub-analysis using data collected for men aged > 65 years only revealed accuracies as high as 0.78 ± 0.07 (F1-score: 0.79 ± 0.05). The overall balanced accuracy for classifying subjects with a recent history of falling was 0.57 ± 0.05 (F1-score: 0.62 ± 0.04). The classification of subjects relative to their frailty state primarily relied on features extracted from the plantar pressure series collected during the walking test.ConclusionIn the future, plantar pressures measured with smart insoles inserted in the shoes of senior people may be used to evaluate aspects of frailty related to the physical dimension (e.g., gait and balance alterations), thus allowing assisting clinicians in the early identification of frail individuals.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2022

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