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DTSTART;TZID=Asia/Seoul:20251121T100000
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SUMMARY:Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Modeling personalized heart rate response to exercise and environmental factors with wearables data” by Nazaret et al.\, npj digital medicine\, 2023. \nAbstract \nHeart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However\, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized\, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors\, from area topography to weather and instantaneous workout\nintensity. Our approach ef ciently ts the hybrid model to a large set of 270\,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts\, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness\, such as VO2 max (explained variance\n0.81 ± 0.003). Lastly\, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate\, e.g.\, high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable\, robust\, and expressive models for health applications.
URL:https://www.ibs.re.kr/bimag/event/journal-club-dongju-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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