Decoding HD-EMG Signals for Myoelectric Control - How Small Can the Analysis Window Size be?

Abstract

Objective: The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account.Approach: Combining methods of long and short-term memory (LSTM) and dynamic temporal warping (DTW), we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals.Main Results: Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5% to 17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNN), STW outperformed by 5% to 18%. Also, STW showed enhanced performance when compared to the CNN+LSTM model by 2% to 14%. All differences were statistically significant with a large effect size.Significance: This feasibility study provides evidence supporting the hypothesis that spatio-temporal warping of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

Internet of Things for beyond-the-laboratory prosthetics research

“…Moreover, raw data transmission increases the power consumption, and it will significantly reduce the battery life of the device [43]. Considering these factors, further discussion may be required to determine whether it is worthwhile to transmit raw data or to convert myoelectric signals into features before transmission especially for high-density EMG settings [44][45][46]. If raw EMG signals are indispensable, they can be saved on the on-board SD card and uploaded to the server periodically.…”

Research on Lower Limb Step Speed Recognition Method Based on Electromyography

“…The time window plays an important role in the processing of the EMG signal pattern. Many studies [ 24 , 25 , 26 ] have discussed this problem in depth. Based on the above research, in gait recognition, we need to detect the initial moment of gait and, on this basis, conduct window division.…”

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

“…For the classification model, support vector machine (SVM) [ 8 ], K -nearest neighbour (KNN) [ 19 ], and some other classic algorithms were used frequently in gesture recognition. In [ 21 ], the authors used LDA to explore the impact of varying the number of electrodes and segmentation window sizes on EMG decoding accuracy, as it is the most commonly used for the classification of limb movements [ 22 ]. Hancong et al also utilized LDA for a low-power embedded system in [ 23 ], in that LDA features low requirements for computing power.…”