Abstract
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in real-world environment. The motion artifact suppression algorithms presented in this article, prevent the activation of the prosthesis drive during artifacts, thereby achieving a substantial performance boost. These algorithms classify the signal into muscle contractions and artifacts. Therefore, new time domain features, such as Mean Crossing Rate are introduced and well-established time domain features (e.g., Zero-Crossing Rate, Slope Sign Change) are modified and implemented. Various artificial intelligence models, which require low calculation resources for an application in a wearable device, were investigated. These models are neural networks, recurrent neural networks, decision trees and logistic regressions. Although these models are designed for a low-power real-time embedded system, high accuracies in discriminating artifacts to contractions of up to 99.9% are achieved. The models were implemented and trained for fast response leading to a high performance in real-world environment. For highest accuracies, recurrent neural networks are suggested and for minimum runtime (0.99–1.15 s), decision trees are preferred.
Highlights
Electromyography (EMG) sensors are applied, beyond others, for myoelectric prosthesis control, diagnostic purposes and exoskeletons
The state-of-the-art dry sensors, which require a conductive connection to the skin, are typically applied for myoelectric prosthesis control
Please note that these accuracies were calculated for test data with many transitions
Summary
Electromyography (EMG) sensors are applied, beyond others, for myoelectric prosthesis control, diagnostic purposes and exoskeletons. The state-of-the-art dry sensors, which require a conductive connection to the skin, are typically applied for myoelectric prosthesis control. Well-established EMG features have been implemented [6,7,8] and different signal processing algorithms have been developed [9,10,11]. These algorithms achieve high accuracies for distinguishing hand movements, they often lack robustness in real-world environment [12,13]. In terms of practical application, robustness is preferred over technologically complex and unreliable systems [14]
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