Detecting anomalies in smart wearables for hypertension: a deep learning mechanism

Today's article comes from the Frontiers in Public Health journal. The authors are Reddy et al., from the Stanley College of Engineering and Technology for Women, in India. In this paper, they are putting forward a hybrid deep learning system that combines ResNet and LSTM architectures to predict blood pressure from wearable sensor data. Their goal is to create an anomaly detection system that can identify hypertensive episodes in real-time while, importantly, minimizing false alarms.

DOI: 10.3389/fpubh.2024.1426168