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Gait Balance Detection and Analysis Using Smart-phone Application

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dc.contributor.advisor Pallis, Jani M.
dc.contributor.advisor Faezipour, Miad
dc.contributor.author Vuligonda, Lavanya
dc.contributor.author Donthula, Prithi
dc.date.accessioned 2016-05-26T18:51:14Z
dc.date.available 2016-05-26T18:51:14Z
dc.date.issued 2016-04-01
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1652
dc.description.abstract This poster describes the components of a gait analysis framework in a smart-phone App. Gait detection is a major concern problem in the field of biomedical Engineering. This present work deals with gait detection using the help of the following devices: a 3-axial accelerometer sensor, an Arduino uno kit a GPS-GSM module, and a buzzer. The sensor analyzes the data stream coming from a (Microelectromechanical systems) MEMS tri-axial accelerometer to infer fall occurrences and to evaluate the gait quality. With the signals obtained, a new automated approach is developed for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. For the analysis of different patterns, we use MATLAB. Arduino uno kit is an open source electronics prototyping platform based on flexible and easy-to-use hardware and software modules. The accelerometer helps in tracking human motion and it plays a vital role in gait detection. On the smart-phone side, the application is made of four major components: Background Service, Classification Engine, Notification System and Graphical User Interface. This is a research work in-progress where we are trying to also analyze and process the gait signals from PhysioNet’s Gait in Neurodegenerative disorder public database that includes various kinds of diseases such as Parkinson’s disease, Huntington’s disease, Amyotrophic Lateral Sclerosis and Healthy control. With the signals obtained, in this study, we try to develop a new automated approach for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. en_US
dc.language.iso en_US en_US
dc.subject Arduino en_US
dc.subject Gait analysis en_US
dc.subject Gait detection en_US
dc.subject Micro-electro-mechanical systems (MEMS) en_US
dc.subject Mobile devices en_US
dc.title Gait Balance Detection and Analysis Using Smart-phone Application en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US


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