Article

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.

Langue : ANGLAIS