A Dual-Focus Cloud–Edge Collaborative Framework in Multitask Hemodynamic Parameter Cross-Scale Analysis: The Equilibrium of Clinical Performance and Efficiency

The precise monitoring of hemodynamic parameters is crucial for cardiovascular health assessment and disease prevention. However, current noninvasive hemodynamic monitoring technologies fail to balance performance and efficiency. To overcome these challenges, this study introduces a dual-focus multitask fusion framework built on a sparse multigate mixture-of-experts network (SM2oE) and multiview photoplethysmography (PPG) for the accurate estimation of noninvasive hemodynamic parameters. This framework integrates a multiscale fusion architecture with a hybrid attention mechanism and incorporates an uncertainty regression loss function (UR-Loss) to enhance intertask information fusion and overall model performance. Through structured pruning and bidirectional knowledge distillation, the framework effectively reduces computational costs and ensures model efficiency. Cross-database evaluation demonstrates that the proposed method achieves blood pressure (BP) estimation performance falling within the numerical thresholds specified by IEEE, BHS, and AAMI guidelines. Under a lightweight strategy, the LSM2oE exhibits significant clinical application potential, with mean absolute errors for systolic BP, mean arterial pressure, diastolic BP, and heart rate being 4.02 mmHg, 2.99 mmHg, 3.00 mmHg, and 2.40 bpm, respectively. The parameter counts and FLOPs are 1.26 M and 0.15 Gmac, respectively, surpassing existing baseline models with only 4.0% of the parameter volume of the teacher model. Additionally, leveraging a cloud-edge collaboration framework, we achieved hemodynamic assessment across multiple Internet of Medical Things (IoMT) devices via a local area network and the RV1126 platform. The system demonstrates cross-scale analysis capability and real-time parameter estimation performance in IoMT environments, showcasing extensive application potential.