SoC Architecture for Acquisition and Processing of EMG Signals

EMG (electromyography) is a technique used to measure the electrical activity of muscles during contraction and relaxation and is used in a variety of clinical and research applications. In particular, it is very useful for the diagnosis and evaluation of neuromuscular disorders and for controlling electromechanical devices or prostheses. This signal has a low Signal-Noise Ratio (SNR) and needs to undergo conditioning processes including amplification, filtering and digitization for further processing. In order to improve the SNR of the EMG signals, this work describes a System-on-Chip (SoC) architecture for the acquisition and processing of the EMG signals, offering a modular and high-performance solution. These signals could be applied to the movement of a therapeutic exoskeleton with the aim of improving active rehabilitation therapies for patients with incomplete spinal cord injury. The proposed architecture provides a modular solution that allows signal digitization, which is performed as close as possible to the electrode in order to minimize transmission losses and signal noise. In addition, sampling is performed at a higher sampling rate than commercial acquisition systems, while supporting significant processing throughput. The architecture uses the ADS1298R integrated circuit for multichannel acquisition and performs the correct conditioning of EMG signals, as well as integrates the communication module through Serial Peripheral Interface (SPI) interface to carry out the configuration and data transfers. In this case, the acquired signal is processed using a moving average-based algorithm and its thresholding to establish muscle activity. The identified muscle activity could be used as an active reference to activate the therapeutic exoskeleton. The results show the validation of the proposed architecture with an EMG signal with a sampling rate of 8 ksps.