DailyBeat: reliable cardiac sensing under self-induced dynamic interference using mmWave radar
Abstract
Reliable cardiac monitoring in daily work environments could support applications such as stress assessment and mental workload tracking. Radar sensing provides a promising contactless solution by capturing subtle chest motion without requiring wearable devices. However, many existing methods assume quasi-static conditions and experience substantial performance degradation in office environments because of self-induced interference, including body motion and the often-overlooked effect of irregular respiration. To address these challenges, we analyze the temporal structure of cardiac mechanical activity and identify two key properties: short-duration impulsiveness and short-term quasi-periodicity. Guided by these properties, we propose a signal-processing paradigm for reliable cardiac sensing under dynamic interference. Specifically, we design a bidirectional wavelet transform to extract pulse-like cardiac events from complex radar signals, a periodicity-guided multi-scale fusion strategy to retain rhythmically consistent components, and an adaptive spatial selection method to identify reliable chest reflections. We implement this paradigm as a real-time prototype system, DailyBeat, using a commercial mmWave radar. Experiments across multiple participants and office activities demonstrate that DailyBeat recovers detailed cardiac mechanical waveforms and accurately estimates heart rate, inter-beat intervals, and a QT-related mechanical surrogate, enabling fine-grained and unobtrusive cardiac monitoring in daily office environments
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BibTeX
@article{chang2026dailybeat,
title = {{DailyBeat: reliable cardiac sensing under self-induced dynamic interference using mmWave radar}},
author = {Zhaoxin Chang and Pei Wang and Xujun Ma and Luan Chen and Fusang Zhang and Duo Zhang and Badii Jouaber and Daqing Zhang},
journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
year = {2026},
month = sep,
doi = {10.1145/3831967},
url = {https://hal.science/hal-05773505v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - DailyBeat: reliable cardiac sensing under self-induced dynamic interference using mmWave radar
AU - Zhaoxin Chang
AU - Pei Wang
AU - Xujun Ma
AU - Luan Chen
AU - Fusang Zhang
AU - Duo Zhang
AU - Badii Jouaber
AU - Daqing Zhang
PY - 2026
DA - 2026/09/30
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
DO - 10.1145/3831967
UR - https://hal.science/hal-05773505v1
AB - Reliable cardiac monitoring in daily work environments could support applications such as stress assessment and mental workload tracking. Radar sensing provides a promising contactless solution by capturing subtle chest motion without requiring wearable devices. However, many existing methods assume quasi-static conditions and experience substantial performance degradation in office environments because of self-induced interference, including body motion and the often-overlooked effect of irregular respiration. To address these challenges, we analyze the temporal structure of cardiac mechanical activity and identify two key properties: short-duration impulsiveness and short-term quasi-periodicity. Guided by these properties, we propose a signal-processing paradigm for reliable cardiac sensing under dynamic interference. Specifically, we design a bidirectional wavelet transform to extract pulse-like cardiac events from complex radar signals, a periodicity-guided multi-scale fusion strategy to retain rhythmically consistent components, and an adaptive spatial selection method to identify reliable chest reflections. We implement this paradigm as a real-time prototype system, DailyBeat, using a commercial mmWave radar. Experiments across multiple participants and office activities demonstrate that DailyBeat recovers detailed cardiac mechanical waveforms and accurately estimates heart rate, inter-beat intervals, and a QT-related mechanical surrogate, enabling fine-grained and unobtrusive cardiac monitoring in daily office environments
SN - 2474-9567
ER -