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Poster: A Privacy-preserving Heart Rate Prediction System for Drivers in Connected Vehicles

Published: 18 June 2023 Publication History

Abstract

The prediction of health metrics for drivers has become increasingly crucial due to the potential impact of drivers' health conditions on traffic accidents. Heart attack is one of the primary causes of health-related traffic tragedies. However, drivers' heart rate (HR) is considered highly-private data, which should not be collected by a centralized server for training the prediction model. To this end, we contribute FedHeart, a novel privacy-preserving federated learning (FL) system for HR prediction. We observe distinct HR changes when drivers are in steady-state and changing-state conditions, and thus we utilize FL to train two separate models for these states. To enhance the prediction accuracy, we incorporate contrastive learning to extract HR features. Through experiments on two real-world datasets, we validate the efficiency of the proposed system in accurately predicting HR during driving scenarios.

References

[1]
Lars Alfredsson, Niklas Hammar, and Christer Hogstedt. 1993. Incidence of Myocardial Infarction and Mortality from Specific Causes among Bus Drivers in Sweden. International Journal of Epidemiology 22, 1 (1993), 57--61.
[2]
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271 (2018).
[3]
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and et al. 2018. Modeling Long-and Short-Term Temporal Patterns with Deep Neural Networks. In Proc. of SIGIR.
[4]
Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. 2021. Prototypical Contrastive Learning of Unsupervised Representations. In Proc. of ICLR.
[5]
Yang Liu, Zhuo Ma, Zheng Yan, Zhuzhu Wang, Ximeng Liu, and Jianfeng Ma. 2020. Privacy-preserving federated k-means for proactive caching in next generation cellular networks. Information Sciences 521 (2020), 14--31.
[6]
Fan Mo, Hamed Haddadi, Kleomenis Katevas, and et al. 2021. PPFL: privacy-preserving federated learning with trusted execution environments. In Proc. of MobiSys.
[7]
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2020. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In Proc. of ICLR.
[8]
Zhihan Yue, Yujing Wang, Juanyong Duan, and et al. 2022. TS2Vec: Towards Universal Representation of Time Series. In Proc. of AAAI.
[9]
Lingchen Zhao, Lihao Ni, Shengshan Hu, and et al. 2018. InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy. In Proc. of INFOCOM.

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  1. Poster: A Privacy-preserving Heart Rate Prediction System for Drivers in Connected Vehicles

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    cover image ACM Conferences
    MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services
    June 2023
    651 pages
    ISBN:9798400701108
    DOI:10.1145/3581791
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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    Published: 18 June 2023

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    Author Tags

    1. heart rate prediction
    2. federated learning
    3. driving scenarios

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    MobiSys '23 Paper Acceptance Rate 41 of 198 submissions, 21%;
    Overall Acceptance Rate 274 of 1,679 submissions, 16%

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