Development of a Method for Evaluating Mobility of the Elderly Using Machine Learning

• Published in: Transactions of Japanese Society for Medical and Biological Engineering Vol. Annual59; no. Abstract; p. 315
• Main Authors: Fushiki, Ryoma, Akiyama, Yoko, Manabe, Yuichiro, Sato, Fuminobu, Fujita, Kazuki
• Format: Journal Article
• Language: Japanese
• Published: Japanese Society for Medical and Biological Engineering 2021
• ISSN: 1347-443X; 1881-4379
• DOI: 10.11239/jsmbe.Annual59.315

One of the early symptoms of musculoskeletal disorders is called "locomotive syndrome" (Locomo). There is a qualitative index called "Locomomo-Degree Test," which can to determine whether a person has Locomo or not, but has issues in safety and simplicity. As a preliminary step to establish an automatic and quantitative evaluation method of Locomo degree, we tried to determine whether a person has Locomo or not by using acceleration sensor and a machine learning model. To investigate a new index to discriminate Locomo from the characteristics of human movement and to obtain the independent variables for machine learning, one-legged stand test and alternate one-legged stand test were conducted for elderly persons. A statistical method, multivariate logistic regression analysis and a machine learning method, Gradient Boosting Decision Tree (GBDT), were used to predict the presence or absence of locomotion. As a result, the performance of GBDT exceeded that of our previous study.