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DOI:
Journal of Software:2010.21(zk):39-50

融合多模信息感知的低功耗行为识别
齐娟,陈益强,刘军发,孙卓
(中国科学院 计算技术研究所 普适计算中心,北京 100190; 中国科学院 研究生院,北京 100049)
Power-Efficient Activity Recognition Based on Multi-Modal Information Sensing and Fusion
QI Juan,CHEN Yi-Qiang,LIU Jun-Fa,SUN Zhuo
(Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China; Graduate University, The Chinese Academy of Sciences, Beijing 100049, China)
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Received:July 15, 2009    Revised:July 09, 2010
> 中文摘要: 行为识别在普适计算领域有着极大的应用前景,可广泛应用于医疗监护、智能家居/办公、商业服务等方面.其中基于传感器的行为识别因其分布范围广、不具侵扰性等优点,已成为目前的研究热点之一.采用机器学习理论和方法,提出了一种基于多模传感信息感知和融合的行为识别层次框架.该框架综合加速度和无线网络信号两种传感器信息、利用多种基于融合的识别方法,能同时解决“用户在哪里”、“用户在做什么”、“用户将要去做什么”等行为相关问题.采用智能手机作为实验平台,利用其内置的多种传感器收集用户的行为信息,更符合普适计算的发展趋势.最后通过实际采集的数据和大量的实验说明了各种方法的有效性.
中文关键词: 行为感知  传感器  机器学习  信息融合
Abstract:Activity recognition has many potential applications in pervasive computing field, such as medical care, intelligent home/office, business service, etc. Recently, sensor based activity recognition has attracted much attention, due to its advantages of ubiquity and less intrusion. This paper studies sensor based activity recognition. In particular, it recognizes high-level user activities from multi-mode sensor data using machine learning methods. This paper proposes an activity recognition framework, which makes use of low-level wireless network signal and acceleration data to answer questions like “where is the user”, “what is the user doing” and “what is the user going to do”. Three main fusion algorithms are included in this framework. Considering the trend of pervasive computing, this paper uses the mobile phones and their embedded sensors to collect activity information. Effectiveness of the proposed algorithms is confirmed on real world collected dataset.
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基金项目:Supported by the National Natural Science Foundation of China under Grant No.90820303 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z305 (国家高技术研究发展计划(863)); the Co-building Program of Beijing Municipal Education Commission of China (北京市教育委员会共建项目) Supported by the National Natural Science Foundation of China under Grant No.90820303 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z305 (国家高技术研究发展计划(863)); the Co-building Program of Beijing Municipal Education Commission of China (北京市教育委员会共建项目)
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齐娟,陈益强,刘军发,孙卓.融合多模信息感知的低功耗行为识别.软件学报,2010,21(zk):39-50

QI Juan,CHEN Yi-Qiang,LIU Jun-Fa,SUN Zhuo.Power-Efficient Activity Recognition Based on Multi-Modal Information Sensing and Fusion.Journal of Software,2010,21(zk):39-50