JMIR Publications

ABSTRACT

Gait quality is generally considered a critical health indicator, especially for older adults. Assessing gait quality in an unobtrusive manner is essential for the seamless monitoring of these populations. However, precise evaluation of gait often requires dedicated sensor systems, which may not be feasible or practical for monitoring in everyday life. Gait quality is generally considered a critical health indicator, especially for older adults. Assessing gait quality in an unobtrusive manner is essential for the seamless monitoring of these populations. However, precise evaluation of gait often requires dedicated sensor systems, which may not be feasible or practical for monitoring in everyday life. We first defined four typical gait patterns of older adults: gait with disturbances, gait without disturbances, gait with cane, and gait with walker. Thereafter, we formulated the classification task of these gait patterns as a supervised machine learning problem in which multivariate time series signals of inertial motions captured while walking were learned and tested using deep neural network architectures in a supervised learning fashion. To demonstrate the feasibility of the proposed approach, we recruited 38 older adult participants (age: 80.4 +- 6.5; 73.7 % women). We also examined the internal representations and computational attention vectors learned by deep neural networks tailored to learn gait features from complex time series gait-related data. The experimental results validated the feasibility of the proposed approach, revealing that 1) gait patterns of older adults were recognized using smartwatch measurements from both-hands with high accuracies (mean F_1-score of 97.4 %) and 2) either left- or right-hand measurement is sufficient for the proposed recognition task (mean F_1-score of 97.1 %). The proposed wearable recognition system demonstrated acceptable performance (i.e., both accuracy and F_m were higher than 0.95), in predicting the gait patterns of the older adult population.