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

• Published in: Annals of biomedical engineering Vol. 51; no. 7; pp. 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: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biochemistry; Biological and Medical Physics; Biomechanical Phenomena; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Controllers; Gait; Humans; Lower Extremity; Movement Disorders; Neural networks; Neural Networks, Computer; Original Article; Real time; Thigh; Walking; Wearable computers; Wearable technology; Gait phase detection; Exoskeleton; Activities classification; Artificial neural networks

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 buil...