Abstract
In recent years, WiFi signal based activity recognition attracts attention in the community. One traction is the ubiquity of WiFi devices. The challenge is to achieve sufficient accuracy with minimal infrastructure cost without compromising user experience, e.g., no device attachment on body. In this work, we propose a novel design paradigm called WiSen, to enhance the performance of the status quo. WiSen is able to fully utilize the channel information in received signals. Behind the scenes, WiSen exploits the diversity across subcarriers in the WiFi band while solving the challenge of dual-statistics analysis. With extensive experiments in typical environments, the dual-statistics scheme enhances the accuracy by 36% over the traditional approach. While, integration with motion augmentation further improves the overall accuracy by 5.2%, achieving 98% of overall accuracy.
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Notes
- 1.
- 2.
This resolution is good enough for human activities, with speed less than 8 m/s.
- 3.
Since the information mainly exists in the first several components of PCA, WiSen does not analyze the later components.
- 4.
It is the maximum value due to the boundary effect.
- 5.
For example, level 1 is the range of 150–300 Hz while level 2 is 75–150 Hz.
- 6.
It is not obvious in the figure due to the scale.
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Zhang, O. (2023). Dual-Statistics Analysis with Motion Augmentation for Activity Recognition with COTS WiFi. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_59
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