Yongqiang Jiang
ABSTRACT
Recently, Wi-Fi-based human activity recognition technique has attracted attentions extensively. Due to its ease of access and low cost, Wi-Fi-based technique achieves great potential on building human activity recognition systems. However, this technique is limited because the Wi-Fi signal is less-informative and susceptible to environmental changes. To build a practical Wi-Fi-base human activity recognition system, in this paper, WiLay, a layer-structured human activity recognition system is proposed. To recognize 7 different activities, WiLay used an activity-oriented process to select and extract features according to the hierarchical relationship between different activities, and trained multiple classifiers to build its layer-structured recognition system. We collected data on several different environments and tested our system. The experimental results with 95.4% accuracy and 89.1% recall rate indicate that our system has very well performance on recognition human activities and is robust to environmental changes.
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Index Terms
- WiLay: building wi-fi-based human activity recognition system through activity hierarchical relationship
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