Sensors, Vol. 19, Pages 3531: A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Sensors, Vol. 19, Pages 3531: A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal Sensors doi: 10.3390/s19163531 Authors: Lorenzo Manoni Claudio Turchetti Laura Falaschetti Paolo Crippa 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 pap...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research