Recognition of Sleep Disorders using IoT-Based Wearables and Neutrosophic Data Analytics
Document Type
Article
Source of Publication
International Journal of Neutrosophic Science
Publication Date
1-1-2024
Abstract
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.
DOI Link
ISSN
Publisher
ASPG Publishing LLC
Volume
23
Issue
2
First Page
211
Last Page
220
Disciplines
Medicine and Health Sciences
Keywords
Health Monitoring Devices, Internet of Medical Things (IoMT), Internet of Things (IoT), Neutrosophic Data Analytics, Sleep Disorders
Scopus ID
Recommended Citation
Taher, Fatma, "Recognition of Sleep Disorders using IoT-Based Wearables and Neutrosophic Data Analytics" (2024). All Works. 6395.
https://zuscholars.zu.ac.ae/works/6395
Indexed in Scopus
yes
Open Access
no