Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control


Current state-of-the-art upper limb myoelectric prostheses are limited by only being able to control a single degree of freedom at a time. However, recent studies have separately shown that the joint angles corresponding to shoulder orientation and upper arm EMG can predict the joint angles corresponding to elbow flexion/extension and forearm pronation/ supination, which would allow for simultaneous control over both degrees of freedom. In this preliminary study, we show that the combination of both upper arm EMG and shoulder joint angles may predict the distal arm joint angles better than each set of inputs alone. Also, with the advent of surgical techniques like targeted muscle reinnervation, which allows a person with an amputation intuitive muscular control over his or her prosthetic, our results suggest that including a set of EMG electrodes around the forearm increases performance when compared to upper arm EMG and shoulder orientation. We used a Time-Delayed Adaptive Neural Network to predict distal arm joint angles. Our results show that our network's root mean square error (RMSE) decreases and coefficient of determination (R(2)) increases when combining both shoulder orientation and EMG as inputs.

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    • "Current efforts using EMG for closed-loop control of lower limb prostheses have focused primarily on classification of EMG signals to identify discrete classes of movement (Au, Berniker et al. 2008, Delis, Carvalho et al. 2009, Hargrove, Huang et al. 2009, Ha, Varol et al. 2011, Hargrove, Simon et al. 2011, Huang, Zhang et al. 2011, Huang and Ferris 2012, Silver- Thorn, Current et al. 2012, Hargrove, Simon et al. 2013, Miller, Beazer et al. 2013, Wentink, Beijen et al. 2013). This emphasis on classification parallels current techniques used in upper limb prosthetic systems to compensate for the uncertainty in mapping a subset of EMG inputs to multiple degrees of freedom and types of movement (Kuiken, Miller et al. 2005, Yatsenko, McDonnall et al. 2007, Kuiken, Li et al. 2009, Artemiadis and Kyriakopoulos 2010, Bueno, French et al. 2011, Pulliam, Lambrecht et al. 2011, Akhtar, Hargrove et al. 2012, Hebert and Lewicke 2012, Jiang, Vest-Nielsen et al. 2012, Muceli and Farina 2012, Jiang, Muceli et al. 2013, Li, Chen et al. 2013). Multi-layer artificial neural networks and SVMs have been used extensively for this purpose in upper extremity prosthetic systems and have been shown to provide accurate discrimination across classes of limb movement, particularly when used in combination with neuro-fuzzy systems and auto-regressive models (Englehart and Hudgins 2003, Karlik, Tokhi et al. 2003, Liu 2007, Au, Berniker et al. 2008). "
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