Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands

• Published in: Journal of rehabilitation research and development Vol. 51; no. 9; pp. 1439 - 1454
• Main Authors: Segil, Jacob L., Controzzi, Marco, Weir, Richard F. ff, Cipriani, Christian
• Format: Journal Article
• Language: English
• Published: United States Department of Veterans Affairs 01.01.2014; Superintendent of Documents
• Subjects: Activities of Daily Living; Adult; Algorithms; Artificial Limbs; Comparative analysis; Control systems; Electrodes; Electromyography; Experiments; Hand; Humans; Implants, Artificial; Movement - physiology; Muscle Contraction; Muscle, Skeletal - physiology; Pattern recognition systems; Posture; Prostheses; Prosthesis; Prosthesis Design; Robotics - instrumentation; Signal Processing, Computer-Assisted; Studies; Task Performance and Analysis; User-Computer Interface; Young Adult; United Kingdom; biomechatronics; electromyography; multigrasp hand; myoelectric control; postural control; transradial prosthesis; finite state machines; prosthetic hand; Southampton Hand Assessment Procedure; EMG
• ISSN: 0748-7711; 1938-1352; 1938-1352
• DOI: 10.1682/JRRD.2014.01.0014

A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p < 0.05) than the finite state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.