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Research at St Andrews

Bayesian inference federated learning for heart rate prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Author(s)

L. Fang, X. Liu, X. Su, J. Ye, S. Dobson, P. Hui, S. Tarkoma

School/Research organisations

Abstract

The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.
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Details

Original languageEnglish
Title of host publicationWireless Mobile Communication and Healthcare
Subtitle of host publication9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings
EditorsJuan Ye, Michael J. O'Grady, Gabriele Civitarese, Kristina Yordanova
Place of PublicationCham
PublisherSpringer
Pages116-130
Number of pages15
ISBN (Electronic)9783030705695
ISBN (Print)9783030705688
DOIs
Publication statusPublished - 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume362 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

    Research areas

  • Bayesian inference, Federated learning, Heart rate prediction, Wearable computing

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ID: 274091840

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