Becky Ng EIT, MHSc candidate, IBBME, TRI,
Dr. Babak Taati, PhD, PEng, U of T DSC, IBBME, TRI
Falls are detrimental to older adults, leading to injury and, in many cases, mortality . Fall risk is increased in older adults with dementia due to the individual’s inability to self-monitor their decline in mobility . One way to reduce this risk is to perform gait assessments and implement mobility aids as required . In today’s healthcare setting its infeasible for clinicians to perform gait assessments often enough to effectively reduce fall risk. This study aims to build on the many recent advances in computer vision, deep learning and, specifically, human pose estimation, by applying these methods for gait assessment. This study aims to correlate parameters extracted from the pose estimation data with clinical gait assessments.
Patient data is collected by recording video of elderly dementia patients on a daily basis in their long term care home. Pose estimation algorithms will be run over the videos in post-processing, and gait parameters will be extracted from the resulting sequence of joint and limb locations. A machine learning regression model will be developed to correlate the extracted gait parameters with a clinical gait assessment protocol which is performed on each study participant by a trained clinician.
By developing a method which uses pose estimation to continuously, unobtrusively and automatically perform gait assessment, it may be possible to create a warning system which, if implemented, could reduce falls. Furthermore, if successful, this technology could be adapted for use in any multitude of applications which require not only mobility, but any movement assessment.
Keywords: computer vision, deep learning, pose estimation, gait analysis, fall risk assessment
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