Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data

Compressed sensing based on rakeness for surface ElectroMyoGraphy

Surface ElectroMyoGraphy (sEMG) is a fundamental tool in medicine, rehabilitation, and prostethics but also made appearance on the consumer world with devices such as the Thalmic lab's MYO. Current state of the art transfers the whole sEMG signal but encounter problems when this signal has to be transferred wirelessly in real-time. To overcome limitations of the current state of the art we propose compressed sensing (CS) as a technique to reduce the size of sEMG data. This work demonstrates the advantage of using a priori knowledge on the sEMG signal by rakeness-based design of a CS acquisition system. Our CS system was shaped on the general purpose data from Physionet and tested on data acquired for a simple hand movement recognition task. Results show that it is possible to significantly reduce the size of transmitted sEMG data while being able to reconstruct good quality signals and recognize hand movemenents