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@article{169329,
        author = {Shikha Jumta and Manni Kumar and Teesha and Sakshi Keshri and Vishal Bhatia and Akshit Rana},
        title = {IoT enabled wearable devices for health monitoring and alert systems using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {669-673},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169329},
        abstract = {Health monitoring systems have been transformed by recent developments in machine learning and the Internet of Things (IoT), especially with wearable technologies. In order to enhance emergency response and real time health monitoring systems, this study investigates the integration of machine learning algorithms with wearable technology enabled by the Internet of Things. This study explores how sophisticated machine learning methods like supervised learning, anomaly detection, and predictive modeling can improve health monitoring, building on the work of Smith et al. (2023), which demonstrated the power of IoT sensors for ongoing physiological data collection. This paper offers insights into how IoT-enabled wearables could revolutionize personal health management, especially for managing chronic diseases, by thoroughly examining existing technologies and approaches. It draws attention to the potential of machine learning in facilitating early detection, enhancing customized treatment, and resolving issues in the quickly developing wearable health technology sector.},
        keywords = {IoT, Wearable Devices, Health Monitoring, Ma- chine Learning, Alert Systems, Chronic Disease Management.},
        month = {November},
        }