A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms
This work addresses the challenge of improving exoskeleton motion accuracy for rehabilitation or assistance in daily activities, but it appears incremental as it builds on existing computational methods by incorporating shoulder movement.
The paper tackles the problem of generating human-like motion paths for upper-limb exoskeletons by accounting for scapulohumeral rhythms, which involve shoulder joint movement during large-scale motions like Activities of Daily Living, and shows that the proposed model reproduces human-like motions based on comparisons with experimental data from healthy subjects.
This paper proposes a computational approach for generation of reference path for upper-limb exoskeletons considering the scapulohumeral rhythms of the shoulder. The proposed method can be used in upper-limb exoskeletons with 3 Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of supporting shoulder girdle. The developed computational method is based on Central Nervous System (CNS) governing rules. Existing computational reference generation methods are based on the assumption of fixed shoulder center during motions. This assumption can be considered valid for reaching movements with limited range of motion (RoM). However, most upper limb motions such as Activities of Daily Living (ADL) include large scale inward and outward reaching motions, during which the center of shoulder joint moves significantly. The proposed method generates the reference motion based on a simple model of human arm and a transformation can be used to map the developed motion for other exoskeleton with different kinematics. Comparison of the model outputs with experimental results of healthy subjects performing ADL, show that the proposed model is able to reproduce human-like motions.