Abstract
BackgroundPattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme.MethodsIn this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system’s performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching.ResultsThe practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures.ConclusionsThis is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.
Highlights
Pattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom
Neither of the subjects had any previous experience with using a prosthesis, but both were familiar with electromyography and prosthesis control technology in general and the research project in particular
For the Clothespin Relocation task, the results were stable after two to three sessions. This indicates that the subjects had learned both the prosthesis behavior in conjunction with this task, and the task itself, for all four control systems
Summary
Pattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. Only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Proportional control allows for small, precise movements as well as rapid, coarse movements. This can be a useful property for a prosthesis system, and it is hypothesized that it will be useful for multifunction prostheses. It is hypothesized that proportional control will enhance the user’s control ability significantly because a continuous relationship between muscular contractions and prosthesis response will allow for more rapid and high-fidelity corrections of movements that deviate from the user’s motor intent. The conventional method for proportional control of multifunction myoelectric prostheses is sequential control, with detection of co-contractions of antagonist muscles for switching between functions [1,2]
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