Keywords and Phrases
Actigraphy; Data Analysis; GENEActiv; Physical Activity Data; Sleep; Statistics
"Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual's activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can be determined for an individual with the help of commercially available fitness devices such as Fitbit, Nike, Apple, and many others, which are activity trackers with accelerometer sensors. But these devices determine sleep duration from a 'Proprietary Algorithm', which processes the movement sensor data. Due to the proprietary nature, in a long-term study, the developer of the algorithm could update and make changes to the algorithm without revealing the details of the update to the user. This affects the measures reported by the algorithm. Hence to determine correct and reliable sleep duration, an Algorithm is developed by directly analyzing the actigraphy signals using time series segmentation. The study was done on a group of 20 healthy Undergraduate students from Missouri University of Science and Technology, whose daily physical activities were recorded using the GENEActiv accelerometer wristwatch worn on the non-dominant wrist. In this thesis, an open source algorithm has been developed using the daily physical activity data to estimate the sleep duration for any individual"--Abstract, page iii.
Guardiola, Ivan G.
Dagli, Cihan H., 1949-
Thimgan, Matthew S.
Engineering Management and Systems Engineering
M.S. in Systems Engineering
Missouri University of Science and Technology
viii, 77 pages
© 2018 Yogesh Deepak Lad, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Lad, Yogesh Deepak, "Analyzing sensor based human activity data using time series segmentation to determine sleep duration" (2018). Masters Theses. 7802.