Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses
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(2019) 16:47
RESEARCH
Open Access
Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses Tamás Kapelner1, Ivan Vujaklija2, Ning Jiang3, Francesco Negro4, Oskar C. Aszmann5, Jose Principe6 and Dario Farina7*
Abstract Background: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control. Keywords: Prosthesis control, EMG decomposition, Neural information, Motor units
Background Myoelectric control methods translate electromyographic (EMG) signals recorded from the residual limb of amputees into commands for prostheses. Thereby time-frequency domain features are used to extract information from the EMG signals about the user’s intent [1]. Current clinical myoelectric control methods use the EMG amplitude as a feature to control one degree of freedom (DoF) at a time, usually with recordings from an antagonistic muscle pair [2]. Recently commercialized pattern recognition algorithms rely on multiple recording sites and classify time-domain (TD) and/or frequency-domain EMG features * Correspondence: [email protected] 7 Department of Bioengineering, Imperial College London, London, UK Full list of author information is available at the end of the article
into movement classes [3]. Lately, regression methods have been proposed that rely on similar features to create a continuous mapping from the muscle space to kinematics, rather than classification into a discrete number of classes [4–7]. Furthermore, a number of studies used features extracted from ad
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