FPGA Implementation of Support Vector Machine for Gait Activity Classification

Abstract

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.