Machine learning for human movement understanding. (2nd July 2020)

• Record Type: Journal Article
• Authors: Yoshikawa, Taizo; Losing, Viktor; Demircan, Emel
• Is Part Of: Advanced robotics. Volume 34:Number 13(2020)
• Page Start: 828
• Page End: 844
• Publication Date: 2020-07-02
• Subjects: Rehabilitation robotics -- simulation technologies -- wearable robotics; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1786724
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Main purpose of this project is to develop fundamental technology for assist robots to recover and maintain human motor skill and to extend scope of human activity. Our goal is to provide a system that adapts to its user's personal behavior patterns in real-time. We aim to develop a continuous collaboration system between the assist robots and the user where both alternatively adjust to each other to maximize the system's utility. To understand human movement, we recorded motion sequence of several tasks for different subjects using motion capture system. Through analysis of human motion data, we extracted a general model by rule-based approach. On the other hand, since such tasks are not feasible with static models, we investigate the potential benefit of supervised online learning in the task of online action classification and Deep Learning in the task of acquiring human motion. Finally, developed system was extended to show its potential effect in ergonomics and in assist robotics. GRAPHICAL ABSTRACT: