TY - GEN
T1 - WiLay
T2 - 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2019
AU - Jiang, Yongqiang
AU - Hu, Hai Miao
AU - Pu, Yanglin
AU - Jiang, Haoran
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/12
Y1 - 2019/11/12
N2 - 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.
AB - 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.
KW - Channel State Information
KW - Human Activity Recognition
KW - Ubiquitous Computing
UR - https://www.scopus.com/pages/publications/85079833356
U2 - 10.1145/3360774.3360812
DO - 10.1145/3360774.3360812
M3 - 会议稿件
AN - SCOPUS:85079833356
T3 - ACM International Conference Proceeding Series
SP - 210
EP - 219
BT - Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery
Y2 - 12 November 2019 through 14 November 2019
ER -