基于SVM的类别增量人体活动识别方法

Human Activity Recognition Method Based on Class Increment SVM

基于人体活动识别(Human Activity Recognition,HAR)的健康监护是发现健康异常的一种重要手段。然而,在日常活动识别中,很难提前获取包含所有可能活动类别的训练样本。当预测阶段出现新增类别时,传统的支持向量机(Support Vector Machine,SVM)会将其错误地分类为已知类别。一个鲁棒的分类器应该能够分辨出新增类别,以便后续区别于已知类别并对其进行处理。文中提出一种基于SVM的类别增量人体活动识别方法,引入超球面的思想,既能高精度地识别已知活动类别,又能检测出新增类别。通过训练得到的多个超球面将整个特征空间进行划分,使分类器具有对新增活动类别的检测能力。实验结果表明,与传统多分类SVM方法相比,该方法能够在不显著降低已知类别分类效果的前提下实现对新增类别的检测,从而提高分类器在开放环境下的人体活动识别能力。

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.