Usability and Interaction Evaluation of a Mixed-Reality Adaptive Control Strategy for Wearable Robotics

Abstract

Robotics has increasingly focused on motion-assisting technologies, such as occupational back support exoskeletons, designed to reduce physical strain while preserving natural movement in rehabilitation, augmentation, and industrial ergonomics applications. Manual material handling tasks involving repetitive lifting and lowering remain a primary contributor to lower back disorders in industrial contexts, motivating the development of active exoskeletons capable of modulating assistance through adaptive control strategies. Existing acceleration-based controllers effectively adapt support to lifting dynamics but typically provide continuous assistance across task phases, including lowering, which may reduce transparency and perceived comfort. Recent advances in mixed reality, computer vision, and hand tracking technologies enable the integration of contextual and interaction cues into the control loop, allowing for more intuitive and selective assistance. In this study, a mixed reality interaction interface for an active back support exoskeleton based on a virtual muscle activity concept is evaluated. The virtual approach controller uses hand tracking to activate assistance during lifting and automatically configures assistance through visual load weight estimation. We experimentally compare this approach with a classical manual data entry interface using subjective, usability, workload, and comfort metrics during standardised and combined lifting tasks. The results indicated higher efficiency and satisfaction with the virtual muscle activity interface.

Keywords: Active Back-support Exoskeleton, Virtual Muscle Activity, Mixed-reality Control, Adaptive Force Assistance, Usability Evaluation

DOI: 10.54941/ahfe1007517