GAIT RECOGNITION OF ME-SVM BASED ON SINGLE ACCELEROMETER

Abstract

Gait recognition based on acceleration sensor signals is more suitable for complex environments than other signal-based methods, which can be used for data acquisition for gait recognition. However, most traditional methods only consider the position matching of multiple accelerometers, ignoring the comfort of the collector. In recent years, support vector machine (SVM) has been widely used in portrait recognition, language, and video processing. It has a flexible and powerful processing ability for nonlinear sequence input, and is a sparse and robust classifier. In this paper, we propose a multi-feature SVM algorithm ME-SVM, which is classified after multi-feature fusion. Usually, in the research of action recognition based on acceleration sensors, researchers classify from a single eigenvalue, but this paper optimizes this and further improves the recognition performance. Simulation and experimental results show that the algorithm has high accuracy and robustness.