Sleep disorders management is paramount for maintaining overall well-being. In response to the limitations of traditional approaches, we present an innovative solution that leverages the synergy of Internet of Medical Things (IoMT) and Gated Recurrent Unit (GRU)-based data analytics, leading to smart sleep monitoring that effectively addresses sleep disorders. The conventional methods of sleep monitoring often fall short due to their inability to provide continuous monitoring and personalized insights tailored to individual needs.Our proposed work offers a comprehensive solution to these challenges by introducing an IoMT-enabled system. This system integrates wearable devices and sensors to continuously gather and transmit sleep-related data in real-time. The uniqueness of our approach lies in the integration of GRU, a sophisticated deep learning algorithm, which excels at capturing and analyzing sequential data patterns. GRU enables us to comprehensively analyze the collected sleep data, accurately classifying sleep stages, detecting anomalies, and predicting potential disruptions. To validate the effectiveness of our IoMT and GRU-based approach, We carried out comprehensive trials with individuals who had been diagnosed with a range of sleep disorders, encompassing conditions such as insomnia, obstructive sleep apnea, and idiopathic narcolepsy. The results demonstrate an impressive average sleep efficiency improvement of 18%. This enhancement is achieved through the generation of personalized interventions that stem from the deep insights derived from the GRU analysis. Our approach thus not only improves sleep efficiency but also offers tailored solutions that address specific sleep-related challenges faced by individuals. The real-time nature of IoMT, combined with the advanced pattern recognition capabilities of GRU, equips individuals with actionable insights, personalized recommendations, and effective solutions, thus significantly enhancing the quality of sleep and overall well-being.


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    Titel :

    Smart Sleep Monitoring: IoMT and GRU for Effective Sleep Disorders Management


    Beteiligte:
    Jenefa, A (Autor:in) / Rithick, S. (Autor:in) / Vishal, A. (Autor:in) / Jesman, T. (Autor:in) / Robert, Richin Christ (Autor:in) / Kuriakose, Bessy M (Autor:in)


    Erscheinungsdatum :

    22.11.2023


    Format / Umfang :

    377555 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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