MSTROKE: Methods of Fall Detection and Data Storage

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Harris, Austin

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu; Wu, Dalei
• College of Engineering and Computer Science

• Biomedical engineering
• Brain-computer interfaces

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/535

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1687