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.
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