Shalini Gupta

Graduation Semester and Year

2016

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Donald Liles

Second Advisor

Shouyi Wang

Third Advisor

Shernette R Kydd

Fourth Advisor

Erick C Jones

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. Although, 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 / Auto-ID technology. The broader impacts of this research are 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 an automated technology can provide real time data and solutions to track sleeping patterns such as, how long and deeply you resting, how often you 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 3 major technologies -1) RFID technology based system, 2) sensors and 3) software that installed on a mobile device. This proposal suggests an idea of developing a wireless wearable RFID enabled scarf for recognizing sleep apneas pattern from 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 Auto-ID technology system. A framework was developed for measuring various types of sleep stages. We suggest that based on results that the RFID enabled SJSL (Shalini Jones Smart Linen) scarf and pillow case system is feasible. The outcomes of this research is a viable framework for developing these types of products.

Keywords

Automated analysis, RFID technology, Sleep disruptions

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

Download

Share

COinS