Human Activity Recognition Method Based on Class Increment SVM

Abstract

Health monitoring based on human activity recognition (HAR) is an important means to discover health abnormalities.However,in daily activity recognition,it is difficult to obtain training samples containing all possible activity categories in advance.When new categories appear in the prediction stage,the traditional support vector machine (SVM) will incorrectly classify them as known category.A robust classifier should be able to distinguish the newly added categories so that they can be processed differently from the known categories.This paper proposes a human activity recognition method based on class increment SVM,and the idea of hypersphere is introduced,which can not only identify known activity categories with high accuracy,but also detect new categories.The multiple hyperspheres obtained through training divide the entire feature space,so that the classifier has the ability to detect newly added activity categories.The experimental results show that compared with the traditional multi-class SVM method,our method can realize the detection of new categories without significantly reducing the classification effect of known categories,thereby improving the classifier's ability to recognize human activity in an open environment.

Keywords

• human activity recognition|support vector machine|hyperspheres|clustering separability|class increment