Deep Learning for Wearable-Based Telemedicine

The rapid development of digital healthcare has significantly transformed modern medical services by enabling continuous, remote, and patient-centered healthcare delivery. Among emerging technologies, wearable devices have become essential components of telemedicine systems due to their ability to collect real-time physiological and behavioral data from individuals in daily life environments. However, the large volume, high dimensionality, and complexity of wearable-generated data create significant challenges for accurate analysis and clinical interpretation using conventional approaches. Deep learning (DL) has emerged as a powerful artificial intelligence technique capable of automatically learning complex patterns and extracting meaningful representations from large-scale biomedical datasets. Recent advances in deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer-based models, have demonstrated promising performance in wearable-based healthcare applications, such as physiological signal analysis, disease prediction, and remote patient monitoring. This review presents a comprehensive analysis of deep learning approaches for wearable-based telemedicine. Future research should focus on developing robust, efficient, and clinically validated deep learning models to facilitate the widespread adoption of wearable-based telemedicine.

کلیدواژه ها:

Deep Learning ، Wearable Devices ، Telemedicine ، Digital Health