Human activity recognition with user-free accelerometers in the sensor networks

Many applications using wireless sensor networks (WSNs) aim at providing friendly and intelligent services based on the recognition of human's activities. Although the research result on wearable computing has been fruitful, our experience indicates that a user-free sensor deployment is more na...

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Bibliographic Details
Main Authors: Shuangquan Wang, Jie Yang, Ningjiang Chen, Xin Chen, Qinfeng Zhang
Format: Conference Proceeding
Language:English
Subjects:
Accelerometers
Humans
Intelligent networks
Intelligent sensors
Support vector machine classification
Support vector machines
Testing
Wearable computers
Wearable sensors
Wireless sensor networks
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Summary:Many applications using wireless sensor networks (WSNs) aim at providing friendly and intelligent services based on the recognition of human's activities. Although the research result on wearable computing has been fruitful, our experience indicates that a user-free sensor deployment is more natural and acceptable to users. In our system, activities were recognized through matching the movement patterns of the objects, to which tri-axial accelerometers had been attached. Several representative features, including accelerations and their fusion, were calculated and three classifiers were tested on these features. Compared with decision tree (DT) C4.5 and multiple-layer perception (MLP), support vector machine (SVM) performs relatively well across different tests. Additionally, feature selection are discussed for better system performance for WSNs
DOI:10.1109/ICNNB.2005.1614831