Wi-Fi-based location-independent human activity recognition with attention mechanism enhanced method

• Main Authors: Ding, Xue, Jiang, Ting, Zhong, Yi, Wu, Sheng, Yang, Jianfei, Zeng, Jie
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Human Activity Recognition; Wi-Fi Sensing
• Online Access: https://hdl.handle.net/10356/165121
• Institution: Nanyang Technological University

Wi-Fi-based human activity recognition is emerging as a crucial supporting technology for various applications. Although great success has been achieved for location-dependent recognition tasks, it depends on adequate data collection, which is particularly laborious and time-consuming, being impractical for actual application scenarios. Therefore, mitigating the adverse impact on performance due to location variations with the restricted data samples is still a challenging issue. In this paper, we provide a location-independent human activity recognition approach. Specifi-cally, aiming to adapt the model well across locations with quite limited samples, we propose a Channel–Time–Subcarrier Attention Mechanism (CTS-AM) enhanced few-shot learning method that fulfills the feature representation and recognition tasks. Consequently, the generalization capability of the model is significantly improved. Extensive experiments show that more than 90% average accuracy for location-independent human activity recognition can be achieved when very few samples are available.