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Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control
  • Shriram Tallam Puranam Raghu ,
  • Dawn MacIsaac ,
  • Erik Scheme
Shriram Tallam Puranam Raghu
University of New Brunswick

Corresponding Author:[email protected]

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Dawn MacIsaac
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Erik Scheme
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Abstract

Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using a LDA or SVM classifier, or a deep LSTM model. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.