Abstract

The convergence of wearable technology, deep learning, and smart sensors is revolutionizing real-time health monitoring and preventive care. Traditionally confined to fitness tracking, modern wearable devices now serve as advanced tools for chronic disease management, mental health assessment, elderly care, and remote patient monitoring. This review explores how deep learning enhances the interpretability and predictive power of bio signals such as ECG, PPG, accelerometry, and skin conductance, enabling timely interventions and personalized healthcare. Advances in convolutional neural networks (CNNs), long short-term memory (LSTM) models, and transformer-based architectures have significantly improved the detection and classification of complex physiological patterns. Wearables now contribute to early diagnosis of cardiovascular anomalies, glucose trend prediction in diabetics, and stress detection, while also supporting post-pandemic applications such as COVID-19 surveillance. In elderly care, deep learning-enabled fall detection and gait monitoring have improved response times and reduced hospitalizations. This review emphasizes the strategic importance of interdisciplinary collaboration across medicine, engineering, and data science. By aligning technical advancements with ethical governance and robust regulation, wearable technologies can transition from consumer gadgets to essential clinical tools. Ultimately, this paradigm shift from reactive care to predictive wellness offers a transformative opportunity to enhance global health outcomes, empower patients, and reduce system-wide burdens through continuous, real-time monitoring and intelligent intervention.