FPGA-based acceleration of high density myoelectric signal processing

In recent years, advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis that is capable of performing training and classification of an amputee´s EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. Using the Xilinx Zynq as a low-cost off-the-shelf reconfigurable processing platform, we present a solution that is able to compute prosthesis control signals from multi-channel EMG input with up to 256 channels with a maximum processing delay of less than a single millisecond. While the presented system is able to perform training as well as classification, most of our efforts were focused on the acceleration of the feature extraction units, achieving a speed-up of 6.7 for feature extraction alone, and 4.8 for the total processing time as compared to a software only solution.