Abstract
Recent advancements in robotics and sensor technology have facilitated the development of myoelectric prosthetic hands (MPHs) featuring multiple degrees of freedom and heightened functionality, but their practical application has been limited. In response to this situation, formulating a control theory ensuring the hand dexterity of highly functional MPHs has garnered marked attention. Progress in this field has been directed toward employing machine-learning algorithms to process electromyogram patterns, enabling a broad spectrum of hand movements. In particular, the practical application of 5-finger-driven MPHs with such control functions to real users remains limited, and their attributes and challenges have not been thoroughly examined. In this study, we developed a 5-finger MPH equipped with pattern recognition capabilities. Through a long-term clinical trial, encompassing task assessments and subjective evaluations via questionnaires, we explored the MPH's range of applications. The task assessments revealed an expanded range of achievable tasks as the variety of motions increased. However, this enhanced adaptability was paralleled by a decrease in control reliability. Additionally, findings from the questionnaires indicated that enhancements in task performance with MPHs might be more effective in reducing workplace-related disability than in improving activities in everyday life. This study offers valuable insights into the long-term clinical prospects and constraints associated with multi-degree-of-freedom MPHs incorporating pattern recognition functionality.