Personalized Symmetrical and Asymmetrical Gait Generation of a Lower Limb Exoskeleton

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 9; pp. 9798 - 9808
• Main Authors: Akkawutvanich, Chaicharn, Manoonpong, Poramate
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive online learning; asymmetrical gait; Asymmetry; Biological system modeling; central pattern generator (CPG); Control methods; Customization; Exoskeletons; Frequency control; Gait; gait rehabilitation; Hip; Legged locomotion; Older people; Rehabilitation; Trajectory; Walking
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3234619

Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gaits. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called "EXOVIS") is proposed in this study. The main control of the framework uses adaptive bio-inspired modular neural mechanisms. These mechanisms include decoupled central pattern generators (CPGs) with Hebbian-based synaptic plasticity and adaptive CPG postprocessing networks with error-based learning. The control method facilitates the rapid online learning of personalized walking gaits described by the walking frequency as well as hip, knee, and ankle joint patterns. The method is verified on a real lower limb exoskeleton system with six degrees of freedom (DOFs) on different subjects under static and dynamic conditions, such as flat terrain and a split-belt treadmill. The results show that the proposed method can not only automatically learn to generate personalized symmetrical gaits, but also asymmetrical gaits, which have not been explicitly shown by other approaches so far.