Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction

• Published in: Annals of biomedical engineering 2023-07, Vol.51 (7), p.1471-1484
• Main Authors: Yu, Shuangyue, Yang, Jianfu, Huang, Tzu-Hao, Zhu, Junxi, Visco, Christopher J., Hameed, Farah, Stein, Joel, Zhou, Xianlian, Su, Hao
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Controllers; Gait; Neural networks; Original Article; Real time; Thigh; Wearable computers; Wearable technology
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-023-03151-y

Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ( rRMSE 0 : 6.3%) and predict 200-ms-ahead ( rRMSE 200 ms : 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.