Personalized gait abnormality detection system
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Gait refers to walking manner of a person, and it is also an indication of neurologicalhealth status of a person. Gait variability can occur due to factors like aging,injuries and diseases. If not notified or diagnosed at an early stage, this variabilityof gait could lead to lifetime abnormality.In this work, we have proposed a smartphone based solution for the task ofcapturing gait and performing abnormality detection on the sensed data. Usingthe built-in accelerometer, we collected walking data from 10 different users,which consisted of both normal and minor abnormalities. Features such as stridetime and stride length were extracted and the sudden changes in the walk weredetected by calculating the extent of deviation of these features between the walkdata. Individual user based threshold value of deviation was estimated and thedetection algorithm performance was evaluated for each of the 10 users.
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