Key result
Neural network-powered chest strap ECG achieves ~99% accuracy for evaluating running intensity.
Why the study?
Traditional ECG monitoring systems are limited by poor comfort and restricted user mobility.
A newly developed chest strap ECG monitoring system combined with a neural network model can accurately track ECG signals and assess exercise intensity with 98.7% accuracy.
May support wearable ECG for exercise monitoring; leaves open prospective clinical validation before adoption.
To address the issues of poor comfort and limited mobility associated with traditional ECG monitoring systems, this study developed a chest strap ECG monitoring system (CEMS) utilizing silver-coated polyamide yarn. This system can continuously capture high-quality ECG signals during daily activities such as walking and running, without restricting the user’s movement. Real-time data display and storage are enabled through a built-in Bluetooth module. Furthermore, leveraging these high-quality ECG signals, a classification model based on a fully connected neural network was constructed to evaluate exercise intensity by analyzing key ECG features. After 100 training epochs, the model achieved a classification accuracy of 98.7% for running intensity. The integration of this model with the CEMS enables effective tracking of ECG signals and accurate assessment of exercise intensity, offering a promising and practical solution for next-generation wearable signal monitoring systems.
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Zhang et al. (2025) studied this question. Chest strap ECG monitoring system (CEMS) and neural network classification model was evaluated on Classification accuracy for running intensity. A chest strap ECG monitoring system integrated with a neural network classification model achieved a classification accuracy of 98.7% for evaluating running intensity.
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