Adaptive Dressing Assistance using a Dual Arm Robot for Individual Physique and Kyphosis

Abstract

One of the significant challenges in the development of care robots is recognizing and adapting to individual differences in users' physiques and symptoms, thereby providing personalized assistance. Among Activities of Daily Living (ADLs), dressing assistance poses a particularly complex problem due to the physical interaction between clothing, the human body, and the robot. This complexity is further amplified in care settings, as the primary users are elderly or disabled individuals, whose physical characteristics vary widely. This study focuses on the physical traits of elderly individuals targeted by dressing assistance robots and proposes an adaptive method that accommodates variations in body size and kyphosis. The proposed approach utilizes Dynamic Movement Primitives (DMPs) to generate dressing trajectories that conform to the user's body, based on skeletal information estimated by a vision sensor. Experiments were conducted with eight healthy individuals of varying body types simulating kyphotic posture, one healthy elderly participant, and one elderly participant with posture-related impairments due to Parkinson's disease. The results demonstrate that, compared to non-adaptive methods, the proposed approach enables more adaptive and less burdensome dressing assistance.