Vi-WiFi-Gate: a WiFi sensing gait recognition method inspired by the image vision field

Tianfu Li; Tangjun Chen; Kunyang Li; Ruicheng Ao

doi:10.1117/12.3013352

29 November 2023 Vi-WiFi-Gate: a WiFi sensing gait recognition method inspired by the image vision field

Tianfu Li, Tangjun Chen, Kunyang Li, Ruicheng Ao

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371L (2023) https://doi.org/10.1117/12.3013352
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

WiFi sensing is a hot research field of the Internet of Things (IoT). Utilizing channel state information (CSI) to locate or recognize posture, gesture, gait and so on has attracted the interest of many research scientists. Although previous studies have achieved some achievements, they have stuck to traditional machine learning methods due to data structure and some other limitations. With the development of hardware technology, there are more sub-carrier sequences in the CSI that can be collected, which makes CSI matrix more like an image rather than a time series. So our team believes that we can learn from deep learning methods in the field of image processing and computer vision to further improve the accuracy of the model. In fact, using gait recognition as an example, we collected more than 1,000 pieces of data from three volunteers, added attention mechanisms to a residual neural network which was pre-trained with ImageNet, and got a better model after multiple epochs of training. Specifically, our deep neural network achieved 94.6% recognition accuracy in multi-object classification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianfu Li, Tangjun Chen, Kunyang Li, and Ruicheng Ao "Vi-WiFi-Gate: a WiFi sensing gait recognition method inspired by the image vision field", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371L (29 November 2023); https://doi.org/10.1117/12.3013352

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KEYWORDS

Gait analysis

Computer vision technology

Machine learning

Neural networks

Pattern recognition

Data modeling

Deep learning

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