Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Sensors

Yan Huang¹  Ting Jiang²  Xue Ding²  Zhiwei Li²  Yi Zhong³ 

[1] Global Big Data Technologies Centre (GBDTC), School of Electrical and Data Engineering, University of Technology Sydney, NSW 2007 Sydney, Australia;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;

关键词: Wi-Fi sensing;    human activity recognition;    location-independent;    meta learning;    metric learning;    few-shot learning;   

DOI  :  10.3390/s21082654

来源: DOAJ

【 摘 要 】

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.

【 授权许可】

Unknown