Abstract
BackgroundSignificant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition.MethodsVariations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study.ResultsMuscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features.ConclusionsSelecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.
Highlights
Electromyographic (EMG) signals represent neuromuscular activity and are effective biological signals for expressing movement intent for external device control
We investigated the general impact of EMG signal variations on 11 commonly used EMG features and identified the most robust EMG feature sets for reliable EMG pattern recognition
Subjects sat comfortably in front of a desk with their elbow resting on an armrest, such that their elbow joint was at a right angle and their hand was level with the top of the desk
Summary
Electromyographic (EMG) signals represent neuromuscular activity and are effective biological signals for expressing movement intent for external device control. When the EMG magnitude is above a set value, the user's movement intent is identified, which triggers the HMI system to drive an external device Such algorithms have been used in robotic devices [5,6,7] and upper-limb prostheses [9], but with limited function. Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use.
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