Dressing Assistance Method for Long-Sleeved Shirts Using End-to-End Motion Prediction

Abstract

This study presents a novel method for helping hemiplegic patients put on a long-sleeved shirt. Our analysis specifically focuses on the behaviors of patients with hemiplegia in the latter half of the dressing phase, which involves moving the garment to the healthy side after dressing the paralyzed side. We propose a novel method to assist this movement. A key aspect of the proposed method is the inclusion of a module capable of predicting the target position of a robot’s end effector using time-series depth images. By combining this module with force-based control, a single-armed robot helps a wearer manipulate their garment according to their movements. Experiments utilizing an articulated manipulator demonstrate the effectiveness of the proposed method.