Implementing a SoC-FPGA Based Acceleration System for On-Board SVM Training for Robotic Transtibial Prostheses

Abstract

This paper presents an acceleration system for on-board support vector machine (SVM) model training for robotic transtibial prosthesis based on system on chip with field-programmable gate array (SoC-FPGA) hardware. A hardware prototype was developed and SVM-based model training algorithm was implemented with high-level synthesis technology. Experiments on a transtibial amputee subject demonstrated that the proposed system provided good speedups over ARM-based implementation for on-board training in six locomotion identification tasks (standing, level-ground walking, ramp ascent, ramp descent, stair ascent, stair descent). Meanwhile, the additional power consumption was not significant and acceptable.