Liang, Yu; Wu, Dalei
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
Stokes are the leading cause of disability in adults in the United States. Falls are preve- lant at all stages of recovery among post-stroke patients, and falls can cause serious or life threatening injuries. In this thesis, multiple fall detections methods are explored in order to minimize the faller’s wait time. This research is an extension to our research on mStroke, a reall-time and automatic mobile health system for post stroke recovery and rehabilitation. The proposed system consists of an application (mobile app) that is paired with bluetooth low energy (BLE) modular sensor devices. The sensors provide real-time accerlation, and gyroscopic data to the mobile application. This data is used to classify fall and non-fall activites performed by the user. The focus of mStroke has been on front-end development of application features. To address back-end long-term storage, a data storage solution for mStroke is investigated.
First and foremost, I want to first thank all of family who have shown me support throughout my time as a graduate student. I want to acknowledge my committee members, Dr. Yu Liang and Dr. Dalei Wu. Finally, I want to express my appreciation to my advisor and mentor, Dr. Mina Sartipi. Her guidence over the past years has been invalueable.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Biomedical engineering; Brain-computer interfaces
viii, 38 leaves
Harris, Austin, "MSTROKE: Methods of Fall Detection and Data Storage" (2017). Masters Theses and Doctoral Dissertations.