Key result
MyWear system detects stress levels from HRV with 98% accuracy.
Why the study?
Continuous real-time monitoring and early prediction of physiological parameters such as stress and heart rate fluctuations are needed to improve cardiovascular disease management and patient care.
Does the MyWear wearable T-shirt with machine learning accurately identify irregular heart rhythms?
Does the MyWear wearable T-shirt with machine learning accurately identify irregular heart rhythms?
The MyWear wearable T-shirt combined with an SVM machine learning model demonstrates high accuracy (98%) in detecting irregular heart rhythms.
May support wearable stress monitoring research; leaves open validation for arrhythmia detection in clinical practice.
This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear-designed as a wearable T-shirt-will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement.
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Prakash et al. (2025) studied Stress (n=499). Support Vector Machine (SVM) model via MyWear vs. Other machine learning models (Naïve Bayes, Logistic Regression, Decision Tree, SGD, KNN) was evaluated on Model accuracy for stress level detection. The MyWear system, utilizing a Support Vector Machine model, detected stress levels based on heart rate variability with an average accuracy of 98%.
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