Pose detection using deep learning for gait assessment

Abstract

This approach of gait analysis helps in solving the problems associated with the health of aged people by concentrating on the frailty and senility syndromes that affect elderly individuals. Deterioration of cognitive and motor abilities is a common result of ageing and has an effect on elderly people's quality of life. Some studies have connected these changes in gait patterns to the decline in cognitive and motor function. As a result, gait analysis is a useful tool for diagnosing senility and frailty diseases. By transferring the pose estimation data to a correct statistical analysis, gait analysis can be carried out utilizing machine learning and computer vision approach, allowing physiotherapists to analyse and treat patients accordingly. The main problem observed is that physiotherapists will analyse stroke patients' walking patterns on their own. As a result, this approach solves the problem by providing an accurate result of the observation. As everything is computerised, proper treatment can be provided with accurate data. A physiotherapist could also use machine learning classification techniques to analyse and classify the observed disorder and correct it. Our study mainly focuses on using machine learning approach instead of traditional sensor based or consultation just by vision. This will serve as a newer and better proposition of the same.