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

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。

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

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.