Study of stability of time-domain features for electromyographic pattern recognition

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.

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

“…The locations were determined by palpation for each subject such that channels 1 and 2 would record thumb flexion, and channels 3 and 4 would record finger flexion. Because displacement of the electrode affects the precision of the estimate [3], all data must be remeasured if for any reason the electrodes must be reattached.…”

“…The present system adopted the following segmentation parameters: segment length, 1024; shift length, 32. Every 32 ms, waveform length [3,4] (WL) was adopted as the feature value for each segmentation of raw sEMG. This type of feature value includes both a time domain feature and a frequency domain.…”

“…While the EEG examines internal human information (i.e., emotion, thinking), the sEMG is used to study human motion [3][4][5][6] because it is generated while voluntary muscle movements. It is highly useful as a machine interface, especially for prosthetics and robotic devices that assist rehabilitation.…”

A New Tool for the Automatic Detection of Muscular Voluntary Contractions in the Analysis of Electromyographic Signals

“…In the time-domain analysis of EMG, the histogram is a valuable feature since it can provide information about the frequency with which the EMG signal reaches various amplitudes ( Zardoshti-Kermani et al, 1995). Also important are the mean, maximum value, standard deviation (STD), root mean square (RMS) and total area of the overall signal ( Bilodeau et al, 1992;Tkach et al, 2010). However, one of the most important parameters to extract from this type of biosignal is the activations.…”

Virtual Control of Prosthetic Hand Based on Grasping Patterns and Estimated Force from Semg

“…During the training course, the disable is asked to take advantages of imagination or phantom limb sensation to carry out his willing action, which is a very complex process in which artificial limb user could easily have psychological pressure by excessive times of training or training time [1,2]. The emergence of virtual reality technology makes it possible for the disables easier to focus on training.…”