Key result
Edge AI wearables reduce latency ~70% vs standard models for highly accurate continuous cardiovascular monitoring.
Why the study?
Traditional wearable systems for cardiovascular monitoring rely on cloud processing, which compromises latency, energy efficiency, and user privacy.
The proposed Edge AI-empowered wearable system provides highly accurate and low-latency continuous cardiovascular monitoring on resource-constrained devices.
Hypothesis-generating for Edge AI wearables in continuous CV monitoring; prospective validation needed before clinical use.
The rising incidence of cardiovascular diseases necessitates the development of real-time, intelligent, and privacy-preserving health monitoring systems. Traditional wearable solutions often rely on cloud processing, which compromises latency, energy efficiency, and user privacy. To address these challenges, this study proposes an advanced IoT-enabled embedded wearable system empowered by Edge AI for continuous cardiovascular health monitoring. The system integrates a novel hybrid deep learning model combining 1D Depthwise Separable Convolution (1D-DSC) and Temporal Attention-Gated Recurrent Unit (TA-GRU) to capture both spatial and temporal dependencies in ECG, PPG, and heart rate signals. This study is implemented on an ARM Cortex-M−based microcontroller using TensorFlow Lite. The model achieves exceptional performance with 99.8 % accuracy, 99.62 % precision, 99.54 % recall, and an AUROC of 0.997. The model size is compressed from 22 KB to 11 KB using post-training quantization (PTQ), reducing inference time from 15 ms to just 7 ms without significant loss in accuracy (a drop of only 0.25 %). A real-time alert system visualizes abnormal conditions through LED color codes and haptic feedback, ensuring immediate user awareness. Compared to state-of-the-art models like CNN, Bi-LSTM, and 1D CNN-LSTM, the proposed method exhibits superior accuracy and 70 % lower latency. This work demonstrates a compelling solution for deploying deep learning models on resource-constrained wearables, enabling energy-efficient, secure, and continuous cardiovascular monitoring in real-world scenarios.
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Rani et al. (2025) studied Cardiovascular diseases. IoT-enabled embedded wearable system with Edge AI (1D-DSC and TA-GRU) vs. CNN, Bi-LSTM, and 1D CNN-LSTM models was evaluated on Model accuracy for cardiovascular health monitoring. An IoT-enabled embedded wearable system using Edge AI achieved 99.8% accuracy and 70% lower latency compared to standard models for continuous cardiovascular health monitoring.
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