Volume 23 , Issue 2 , PP: 211-220, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Fatma Taher 1 *
Doi: https://doi.org/10.54216/IJNS.230217
In the dynamic landscape of healthcare technology, the amalgamation of Internet of Things (IoT) systems and Neutrosophic Data Analytics has heralded a paradigm shift. This study delves deep into this transformative synergy by presenting an innovative IoT-based wearable system design for the recognition of sleep disorders. Our meticulously crafted multilayer cellular system seamlessly integrates IoT devices, data acquisition, cloud computing, and machine learning to unlock a wealth of insights into sleep patterns, their anomalies, and the presence of sleep disorders. Through fair and rigorous experimental comparisons, we unveil the prowess of Long Short-Term Memory (LSTM) within the machine learning realm, showcasing its superior performance over baseline models. The results affirm LSTM's ability to detect sleep disorders with remarkable accuracy, precision, and recall, revolutionizing sleep medicine and healthcare practices. This research, at the crossroads of innovation and healthcare, not only illuminates the path to advanced sleep disorder diagnosis but also heralds a new era of personalized healthcare interventions and remote monitoring solutions. As we navigate the realm of IoT and data-driven healthcare, our findings hold the promise of improving the quality of life for countless individuals, reaffirming the pivotal role of technology in safeguarding one of the most fundamental aspects of human well-being – a peaceful and restorative night's sleep.
Sleep Disorders , Internet of Things (IoT) , Neutrosophic Data Analytics , Health Monitoring Devices , Internet of Medical Things (IoMT).
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