Design and implementation of IMU-based locomotion mode recognition system on Zynq SoC

Abstract

Active prostheses are becoming an increasingly viable option for lower limb amputees, as they can significantly improve their quality of life and mobility. However, to ensure a robust and effective control of these prostheses, the terrain environment must be considered. In this study, we have proposed a hardware implementation of a real-time low-latency human Locomotion Mode Recognition (LMR) system on a system on chip (SoC) platform from Xilinx, which can be customized and optimized based on the user's needs without requiring external computing resources or human intervention. The system uses a single IMU sensor placed on the shank, and includes all the stages in a LMR process, i.e. signal pre-processing, feature extraction, and terrains estimation using a MLP neural network classifier to classify five terrains (level walking, stair ascent/descent, ramp ascent/descent). To achieve a flexible and efficient hardware design, the proposed system architecture was optimized using parallelism and quantization optimization techniques. This approach has resulted in a significant performance improvement, with a processing speed 15 times faster than the PL non-optimized approach, and 4.3 times faster than the PS(-O3) optimized implementation on the same Zynq FPGA device. The proposed architecture was also validated in real time via the analog discovery device.