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。
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.
作者
王杨
赵红东
WANG Yang;ZHAO Hongdong(School of Electronics and Information Engineering,Hebei University of Technology,Tianjin 300401,China;Institute of Information Technology,Handan University,Handan Hebei 056005,China)
出处
《计算机应用》
CSCD
北大核心
2020年第3期665-671,共7页
journal of Computer Applications
基金
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)~~
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
human activity recognition
Particle Swarm Optimization(PSO)
context-awareness
machine learning
Support Vector Machine(SVM)