A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Abstract

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

“…EEG signal is carried out when an instruction is transmitted to or received from emotional, visual, auditory, sensory, or muscular stimuli. Therefore, the research in the EEG signal is carried out in various fields such as robotics, medical engineering, and image processing [ 39 , 40 , 41 ]. EEG signal is used to recognize the voice by measuring its frequency through potential differences generated from the temporal lobe [ 8 , 9 ].…”

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals

“…Electromyography (EMG) is a diagnostic method, which enables recording of bioelectric signals resulting from the activities of the skeletal muscles [ 2 , 3 ]. It is often performed while stimulating the relevant motor and peripheral nerves.…”

“…In order to make such systems efficient compressed sensing, or compressive sampling (CS), is applied. The CS is a method for data acquisition, where only few incoherent measurements are required in order to compress sparse in some domain signals [ 3 , 11 , 12 , 13 ]. In [ 11 , 12 , 13 ], the authors showed interesting solutions for compressing both ECG (electrocardiography) and EMG data.…”

“…In [ 11 , 12 , 13 ], the authors showed interesting solutions for compressing both ECG (electrocardiography) and EMG data. In [ 3 ], the authors focused on CS for the purpose of the EMG signals reconstruction in order to design efficient low-powers WBANs. Their work compares four different algorithms applied in practical implementations.…”

Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

“…The advances in deep learning allows extracting features from signals that was not possible using conventional algorithms due to artefacts and noise. This also has been experienced with the use of AI for extracting meaningful signals from electromyography (EMG) measurements [8].…”