Compressive sensing based classification of intramuscular electromyographic signals

Upper extremity prosthetic limbs have succeeded in providing people affected by disabilities such as amputation or paralysis the ability to perform simple manual tasks. Typically, prosthetic limbs are controlled by electromyography (EMG) signals read from the muscles of the patient. As the capabilities of prosthetic hands improve toward those of the intact human hand, their mechanical complexity increases, making the development of advanced techniques for reading and interpreting these EMG signals, while pushing down the power consumption of the sensing device is becoming more critical. In this work, we investigate the classification EMG signals acquired using the technique of compressive sensing, which provides solutions for reducing sensor power and complexity by relaxing the constraints posed by the Shannon sampling theorem on the rate at which the analog signals, in general, should be sampled for preserving the signal's information. We show that using compressive sensing, we can reduce the sampling rate by at least 10 times while maintaining classification accuracy higher than 95%.