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Automatic Gait Balance Detection System

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dc.contributor.author Pallis, Jani M.
dc.contributor.author Shah, Parth
dc.contributor.author Faezipour, Miad
dc.date.accessioned 2017-04-06T13:10:55Z
dc.date.available 2017-04-06T13:10:55Z
dc.date.issued 2017-03-24
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1904
dc.description.abstract Falls are one of the major causes of injury in elderly people over 65 years. Each year, 2.5 million people are treated for a serious fall injury. In addition to the fall there is the delay in receiving assistance. Researchers have developed three methodologies to detect falls: image/video processing by implementing cameras and trackers, acoustic recognition using floor and wall sensors and by analysis of wearable sensors. Fall detection using smartphones has also been proposed in the past. A smartphone may have many constraints due to which a wearable device is a much more viable option for such a critical issue. This poster aims to suggest an effective way to detect falls by using a wearable device of which the major components are: 3-axial accelerometer, Arduino Uno, and GPS-GSM device. Apart from that, a buzzer is also integrated to notify people nearby for assistance. The location of the wearable device also affects the acceleration and the result of fall and motion detection. Although the wrist is the most common body part for any wearable device, the acceleration signal may vary widely. It is efficient to place the device in areas of least movement like a knee or waist. en_US
dc.language.iso en_US en_US
dc.subject Detection en_US
dc.subject Fall detection en_US
dc.subject Mobile application en_US
dc.title Automatic Gait Balance Detection System 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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