Absrt act:Being able to recognize human activities is essential for several intelligent applications , including personal assistive ro-botics and smart homes .In this paper , we perform the recognition of the human activity based on the combined SVM&HMM in daily living environments .Firstly, we use a RGBD sensor ( Microsoft Kinect ) as the input sensor , and extract a set of the fusion features, including motion, body structure features and joint polar coordinates features .Secondly, we propose a combined SVM&HMM Model which not only combines the SVM characteristics of reflecting the difference among the samples , but also de-velops the HMM characteristics of dealing with the continuous activities .The SVM&HMM model plays their respective advantages of SVM and HMM comprehensively .Thus, the combined model overcomes the drawbacks of accuracy , robustness and computa-tional efficiency compared with the separate SVM model or the traditional HMM model in the human activity recognition .The ex-periment results show that the proposed algorithm possesses the better robustness and distinction .%人体行为识别对于个人辅助机器人和智能家居等一些智能应用，是非常必要的功能，本文运用SVM＆HMM混合分类模型进行日常生活环境的人体行为识别。首先，使用微软的Kinect（一种RGBD感应器）作为输入感应器，提取融合特征集，包括运动特征、身体结构特征、极坐标特征。其次，提出SVM＆HMM模型， SVM＆HMM二级模型发挥了SVM和HMM各自的优点，既结合了SVM适于反映样本间差异性特点，又发挥了HMM适合处理连续行为的特点。该二级模型克服了单一SVM模型、传统HMM模型和在人体复杂和相似行为建模过程中精度、鲁棒性和计算效率上的不足。通过大量实验，结果表明SVM＆HMM二级模型对室内日常行为的识别具有较高的识别率，且具有较好的区分性和鲁棒性。
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机译：作为基于PC的测量供应商超过20年的经验，探索ni独有的基于PC的测量的吸引力近年来，许多测量仪器制造商开始使用“基于PC的测量”这一术语。美国国家仪器公司（National Instruments，以下简称ni）是基于pc的测量市场的先驱，其为基于pc的测量提供了称为“ pc + pc接触式/插入式测量仪”的解决方案已有20多年的历史了。在这里，我们询问了基于PC的测量的吸引力，这是nl所独有的。
