Author
Yin, Jiawei
Other Contributors
Julius, Anak Agung; Wen, John T.; Karlicek, Robert F.; Pequito, Sergio; Hicken, Jason;
Date Issued
2020-08
Subject
Electrical engineering
Degree
PhD;
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
Abstract
The circadian rhythm functions as a master clock that regulates numerous physiological processes in humans, e.g., the sleep-wake cycle. The circadian rhythm disorder is closely linked to a wide range of health problems. The first part of this work addresses the light-based minimum-time circadian rhythm entrainment problem of various circadian rhythm models. Being the most powerful stimulus in the circadian rhythm system, light has been widely used as the input of circadian rhythm entrainment problems. Based on optimal control theory and variational calculus, the optimal solution algorithm is proposed for the minimum-time entrainment of various circadian rhythms models, i.e., human core body temperature model and circadian gene regulation models of mammal, Neurospora, and Drosophila. The sleep schedule is jointly optimized with light input to improve the implementability of the entrainment process. The second part of this work is on the estimation of the circadian phase and sleep state using actigraphy data. Signal processing algorithms for these estimation problems are presented along with experimental validation. Concussion detection based on the circadian and sleep features extracted from actigraphy is also discussed. The third part of this work solves the light and sleep schedule optimization for alertness optimization. This work shows that the subjective alertness during night-shifts and mission periods can be enhanced by optimizing the light and sleep schedules.;
Description
August 2020; School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering;
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection;
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;