Abstract

The proposed research suggests a solution for female sleep disruption by using automated analytics and attempts to improve our understanding of sleep disruption physiology. Sleep disruption monitoring is gaining attention with the use of sleep monitoring devices that track sleep disruptions. Automated technology can capture repeated measurements, evaluate sleep patterns, and make suggestions. However, facts revealed by clinical research suggest that it can measure sleep-disruption records and sleep disorders. Moreover, this research can be useful for prescribing individual treatment and hence improve individual healthcare optimization. In fact, some of the health tracking electronic devices assist individuals in tracking their sleep performance. Conceptually, our proposed research methodology provides a systematic analysis procedure for monitoring sleep disruption using RFID/AutoID technology. The broader impact of this research is consistent and precise sleep disruption monitoring; it analyzes brain activities during different sleeping stages, and it provides daily sleep scores and charts that help the end user understand their sleep patterns and synchronize information with their smartphone RFID technology. RFID-based automated technology can provide real-time data and solutions to track sleeping patterns such as how long and deeply you are resting, how often you are getting up, and your brain resting activities. Sleeping well is imperative to a healthy body as well as for effective brain functions. However, persistent sleep disturbance can affect mood, energy levels, and ability to face stressful situations. Neglecting sleep relevant issues may cause serious health ailments, increase the risk of accidents, and impaired relationships. Overall sleep is as necessary as other aspects to physical health. This crucial issue of sleep disruption can be improved by adopting a step-by-step procedure: (1) measure symptoms and sleep patterns, (2) provide sleep record analytics, and (3) provide sleep performance matrices. Accordingly, healthy changes can be made to daytime habits and bedtime routines. In this research, we will attempt to investigate the following research on impacts of sleep disruptions in women through automated analysis. This research is a comprehensive analysis depicting a framework for developing wearable scarves and pillow case linens that can monitor sleep disruption in women. This system’s functioning is mainly based on three major technologies:(1) RFID technology-based system, (2) sensors, and (3) software installed on a mobile device. This proposal suggests an idea of developing a wireless wearable RFID-enabled scarf for recognizing sleep apnea pattern from electroencephalograph(EEG) signals, which identifies sleep disruption in women. The suggested system of sleep monitoring detection uses a combination of EEG recording sensors, electronics, filters, transducers, software, RFID-based AutoID technology system. A framework was developed for measuring various types of sleep stages. We suggest based on the results that the RFID-enabled SJSL (Shalini Jones Smart Linen) scarf and pillow case system is feasible. The outcome of this research is a viable framework for developing these types of products.