FPGA Implementation of Support Vector Machine for Gait Activity Classification

People with lower limb amputations suffer from mobility limitations that degrade their quality of life. In this paper, we propose a hardware system dedicated to human walking activity recognition for smart prostheses, which uses a support vector machine (SVM) algorithm and time domain features to perform activity classification. To achieve a flexible and efficient hardware design, the architecture is implemented on FPGA Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been done through a comparative study, the comparison has been done between floating point MATLAB results and fixed point XSG results.