Human-Centered Evaluation of EMG-Based Upper-Limb Prosthetic Control Modes

The aim of this study was to experimentally test the effects of different electromyographic-based prosthetic control modes on user task performance, cognitive workload, and perceived usability to inform further human-centered design and application of these prosthetic control interfaces. We recruited 30 able-bodied participants for a between-subjects comparison of three control modes: direct control (DC), pattern recognition (PR), and continuous control (CC). Multiple human-centered evaluations were used, including task performance, cognitive workload, and usability assessments. To ensure that the results were not task-dependent, this study used two different test tasks, including the clothespin relocation task and Southampton hand assessment procedure-door handle task. Results revealed performance with each control mode to vary among tasks. When the task had high-angle adjustment accuracy requirements, the PR control outperformed DC. For cognitive workload, the CC mode was superior to DC in reducing user load across tasks. Both CC and PR control appear to be effective alternatives to DC in terms of task performance and cognitive load. Furthermore, we observed that, when comparing control modes, multitask testing and multifaceted evaluations are critical to avoid task-induced or method-induced evaluation bias. Hence, future studies with larger samples and different designs will be needed to expand the understanding of prosthetic device features and workload relationships.

I. Introduction

Upper-limb amputation causes a permanent disability. Basic activities of daily living (ADL), such as grasping, eating, and using zippers on clothing, become difficult to perform for individuals with upper-limb amputations [1]. To restore their motor function and improve the quality of life, advanced prosthesis technology is needed. Over the past decade, significant technological advances have made powered, dexterous prosthetic hands and arms commercially available. The key challenge in making these modern devices functional for upper-limb amputees is an intuitive human–machine interface for easy prosthesis operation. Since electromyographic (EMG) signals represent the user's movement intent, EMG signals recorded from residual muscles have been widely used as neural sources in human–machine interfaces for powered prosthetic arm control [2].