In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities. Existing learning-based RF sensing systems require collecting massive RF training data, which depends heavily on the particular sensing setup/involved activities. Thus, new data needs to be collected when the setup/activities change, significantly limiting the practical deployment of RF sensing systems. On the other hand, recent years have seen a growing, massive number of online videos involving various human activities/motions. In this paper, we propose to translate such already-available online videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup. To validate our proposed framework, we conduct a case study of gym activity classification, where CSI magnitude measurements of three WiFi links are used to classify a person's activity from 10 different physical exercises. We utilize YouTube gym activity videos and translate them to RF by simulating the WiFi signals that would have been measured if the person in the video was performing the activity near the transceivers. We then train a classifier on the simulated data, and extensively test it with real WiFi data of 10 subjects performing the activities in 3 areas. Our system achieves a classification accuracy of 86% on activity periods, each containing an average of 5.1 exercise repetitions, and 81% on individual repetitions of the exercises. This demonstrates that our approach can generate reliable RF training data from already-available videos, and can successfully train an RF sensing system without any real RF measurements. The proposed pipeline can also be used beyond training and for analysis and design of RF sensing systems, without the need for massive RF data collection.
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Multimodal Context-Based Continuous Authentication
We present a new multimodal, context-based dataset for continuous authentication. The dataset contains 27 subjects, with an age range of [8, 72], where data has been collected across multiple sessions while the subjects are watching videos meant to elicit an emotional response. Collected data includes accelerometer data, heart rate, electrodermal activity, skin temperature, and face videos. We also propose a baseline approach for fair comparisons when using the proposed dataset. The approach uses a combination of a pretrained backbone network with supervised contrastive loss for face. Time-series features are also extracted, from the physiological signals, which are used for classification. This approach, on the proposed dataset, results in an average accuracy, precision, and recall of 76.59%, 88.90, and 53.25, respectively, on electrical signals, and 90.39%, 98.77, and 75.71, respectively on face videos.
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- Award ID(s):
- 2039373
- NSF-PAR ID:
- 10495773
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Location:
- Ljubljana, Slovenia
- Sponsoring Org:
- National Science Foundation
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