A Zynq-Based Dynamically Reconfigurable High Density Myoelectric Prosthesis Controller

Alexander Boschmanna, Georg Thombansenb, Linus Witschenc, Alex Wiensd and Marco Platznere
Department of Computer Science, Paderborn University, 33098 Paderborn, Germany.


The combination of high-density electromyographic (HD EMG) sensor technology and modern machine learning algorithms allows for intuitive and robust prosthesis control of multiple degrees of freedom. However, HD EMG real-time processing poses a challenge for common microprocessors in an embedded system. With the goal set on an autonomous prosthesis capable of performing training and classification of an amputee's HD EMG signals, the focus of this paper lies in the acceleration of the computationally expensive parts of the embedded signal processing chain: the feature extraction and classification. Using the Xilinx Zynq as a low-cost off-the-shelf system, we present a solution capable of processing 192 HD EMG channels with controller delays below 120 milliseconds, suitable for highly responsive real-world prosthesis control, achieving speed-ups up to 2.8 as compared to a software-only solution. Using dynamic FPGA reconfiguration, the system is able to trade off increased controller delay against improved classification accuracy when signal quality is decreased due to noisy channels. Offloading feature extraction and classification to the FPGA also reduced the system's power consumption, making it more suitable to be used in a battery-powered setup. The system was validated using realtime experiments with online HD EMG data from an amputee to control a state-of-the-art prosthesis.

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