Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control

TL;DR

This paper evaluates and compares various post-processing techniques for myoelectric control, introducing two new methods that leverage decision changes to improve robustness during dynamic transitions.

Contribution

It proposes two novel temporally aware post-processing schemes that enhance decision robustness by utilizing decision change information in myoelectric control.

Findings

DCIR outperforms existing schemes in error rates

Improved robustness during steady-state and transitions

Temporal context enhances decision reliability

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 conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.