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Abstract
Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.
Highlights
Introduction us criThe surface electromyographic (EMG) signal has been used as the control source for multifunction prostheses for decades
Only signals generated by single-DOF activations of one of the subjects were used for factorization, according to the needs for nonnegative matrix factorization (NMF)
The proposed sparse NMF (SNMF) approach eliminates this need since, from a set of EMG signals generated by an arbitrary task, it selects the factorization with maximum sparseness, which is the one corresponding to basis functions associated to the single-DOFs
Summary
The surface electromyographic (EMG) signal has been used as the control source for multifunction prostheses for decades. In this study we will focus on myoelectric control for wrist function replacement, given its clinical relevance. State-of-the-art wrist-control prosthetic hands employ machine learning techniques to extract the neuromuscular control information from the surface EMG. Researchers have proposed several control schemes based on pattern recognition techniques [1], and have made progresses in enhancing the control accuracy by spatial filtering [2], sensory feedback [3,4], and approaches insensitive to shifting and rotation of the electrodes [5]. Targeted muscle reinnervation is currently a successful clinical solution for prosthetic control in high-level amputations, that can be combined with pattern recognition [6,7,8]. With respect to the classic sequential control scheme of pattern recognition, natural limb movements consist in the simultaneous activations of multiple
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