A Simple and Efficient Near-lossless Compression Algorithm for Surface ElectroMyoGraphy Signals

In this paper, a novel near-lossless compression algorithm meant for electromyography (EMG) signals is proposed and its performance is evaluated towards real EMG measurements. Differently from other near-lossless algorithms, the proposed one does not rely on either matrix decompositions or complex transformations but exploits only a straight-forward dynamic range compression and a simple encoding technique. Therefore, considering its inherent low complexity and low memory requirements, it can be easily implemented in resources constrained microcontrollers as those included in low-cost measurement instruments and e-Health Internet of Things applications. The algorithm has been tested on a dataset including dynamic EMG measurements carried out in a real-world measurement campaign on 8 different subjects, where, for each subject, the EMG signals were recorded from 8 different muscles during a pedaling session. Analytical and experimental results revealed that the proposed compression technique is able to achieve a compression ratio (CR) up to 80% with a percentage root mean square distortion (PRD) in the range between 0.34% and 13.7%. Moreover, differently from the other compression algorithms described in the literature, the proposed one allows fixing the maximum absolute error a priori thus making it possible to control and limit the desired distortion level besides the compression procedure.