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WiFi-based Person Identification Through Motion Analysis | IEEE Conference Publication | IEEE Xplore

WiFi-based Person Identification Through Motion Analysis


Abstract:

By leveraging the advances in wireless communications networks and their ubiquitous nature, sensing through communication technologies has flourished in recent years. In ...Show More

Abstract:

By leveraging the advances in wireless communications networks and their ubiquitous nature, sensing through communication technologies has flourished in recent years. In particular, Human-to-Machine Interfaces have been exploiting WiFi IEEE 802.11 networks to obtain information that allows Human Activity Recognition. In this paper, we propose a classification model to perform Person Identification (PI) through Body Velocity Profile time series, obtained by combining Channel State Information containing gesture knowledge from multiple Access Points. Through this model, we investigate the impact of different gestures on PI classification performance and explore how informing the model about the input gesture can enhance classification accuracy. This information may enable the network to adjust to the absence of features capable of adequately characterizing the desired classes in certain gestures. A simplified stacking model is also presented, capable of combining the softmax outputs of K previously proposed individual models. By having the individual models’ evaluations of a gesture and the gesture information relating to it, the number of gestures considered was shown to significantly improve the performance of the PI classification task. This enhancement increased 17 \% of the average F 1 scores when compared to the individual model tested on the same data.
Date of Conference: 08-11 July 2024
Date Added to IEEE Xplore: 12 August 2024
ISBN Information:
Conference Location: Madrid, Spain

Funding Agency:


References

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