Human activity recognition based on improved particle swarm optimization-support vector machine and context-awareness

doi:10.11772/j.issn.1001-9081.2019091551

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 665-671.DOI: 10.11772/j.issn.1001-9081.2019091551

• Artificial intelligence • Previous Articles Next Articles

Human activity recognition based on improved particle swarm optimization-support vector machine and context-awareness

WANG Yang^1,2, ZHAO Hongdong¹

1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;
2. Institute of Information Technology, Handan University, Handan Hebei 056005, China

Received:2019-09-09 Revised:2019-10-24 Online:2020-03-10 Published:2019-10-29
Supported by:
This work is partially supported by Fund of Electro-Optical Information and Security Control Key Laboratory (614210701041705), the Science and Technology Research and Development Program of Handan (1621203035).

基于改进粒子群优化的支持向量机与情景感知的人体活动识别

王杨^1,2, 赵红东¹

1. 河北工业大学电子信息工程学院, 天津 300401;
2. 邯郸学院信息工程学院, 河北邯郸 056005

通讯作者: 赵红东
作者简介:王杨(1982-),男,河南安阳人,讲师,博士研究生,主要研究方向:智能信息处理、机器学习;赵红东(1968-),男,河北沧州人,教授,博士生导师,博士,主要研究方向:半导体光电子学。
基金资助:
光电信息控制和安全技术重点实验室基金资助项目（614210701041705）；邯郸市科学技术研究与发展计划项目（1621203035）。

Abstract

Abstract: Concerning the problem of low accuracy of human activity recognition, a recognition method combining Support Vector Machine (SVM) with context-awareness (actual logic or statistical model of human motion state transition) was proposed to identify six types of human activities (walking, going upstairs, going downstairs, sitting, standing, lying). Logical relationships existing between human activity samples were used by the method. Firstly, the SVM model was optimized by using the Improved Particle Swarm Optimization (IPSO) algorithm. Then, the optimized SVM was used to classify the human activities. Finally, the context-awareness was used to correct the error recognition results. Experimental results show that the classification accuracy of the proposed method reaches 94.2% on the Human Activity Recognition Using Smartphones (HARUS) dataset of University of California， Irvine (UCI), which is higher than that of traditional classification method based on pattern recognition.

Key words: human activity recognition, Particle Swarm Optimization (PSO), context-awareness, machine learning, Support Vector Machine (SVM)

摘要： 针对目前人体活动类别识别准确率偏低的问题，提出一种支持向量机（SVM）与情景分析（人体运动状态转换的实际逻辑或统计模型）相结合的识别方法，对人体日常的六种活动（步行、上楼、下楼、坐下、站立、躺下）进行识别。该方法利用了人体活动样本之间存在逻辑关系的特点，首先使用经改进的粒子群优化（IPSO）算法对SVM模型进行优化，然后利用优化后的SVM对人体活动进行分类，最后通过情景分析的方法对错误的识别结果进行修正。实验结果表明，所提方法在加州大学欧文分校（UCI）的人体活动识别数据集（HARUS）上的分类准确率达到了94.2%，高于传统的仅使用模式识别进行分类的方法。

关键词: 人类活动识别, 粒子群优化, 情景感知, 机器学习, 支持向量机

CLC Number:

TP391.4

WANG Yang, ZHAO Hongdong. Human activity recognition based on improved particle swarm optimization-support vector machine and context-awareness[J]. Journal of Computer Applications, 2020, 40(3): 665-671.

王杨, 赵红东. 基于改进粒子群优化的支持向量机与情景感知的人体活动识别[J]. 计算机应用, 2020, 40(3): 665-671.

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Human activity recognition based on improved particle swarm optimization-support vector machine and context-awareness

基于改进粒子群优化的支持向量机与情景感知的人体活动识别

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