Polynomial classification model for real-time fall prediction system

Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection.