Deep Learning-Based Heart Rate Estimation from Wrist-Worn Photoplethysmography under Dynamic Motion Conditions
Keywords:
deep learning; heart rate estimation; photoplethysmography; motion artifacts; wearable systems; socio-technical governance; deployment infrastructureAbstract
Wrist-worn photoplethysmography has become a widely used sensing approach for continuous heart rate monitoring, but motion artifacts under dynamic physical activity remain a major obstacle to reliable estimation. This article examines deep learning-based heart rate estimation from wrist-worn photoplethysmography as an infrastructural and socio-technical challenge rather than an isolated algorithmic task. The discussion integrates sensing principles, data infrastructure, system architecture, deep model design, robustness, deployment governance, fairness, and sustainability. It argues that accurate heart rate estimation under motion cannot be achieved solely by increasing model capacity or enlarging training datasets. Instead, reliable systems require layered architectures that coordinate signal quality assessment, uncertainty representation, motion compensation, efficient on-device inference, and post-deployment monitoring. The paper analyzes structural trade-offs among compact embedded models, hybrid edge-cloud configurations, and federated learning frameworks, while also addressing regulatory expectations and population-level equity. Special attention is given to distributional shifts, out-of-distribution detection, calibration, and the environmental costs of wearable machine learning systems. The analysis shows that deep learning can substantially improve motion-tolerant heart rate estimation when it is embedded within a governance framework that supports auditability, fairness, and continuous validation. Future research directions include self-supervised learning, adaptive sensing, subgroup-specific evaluation, and cross-disciplinary strategies for accountable deployment.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
