Machine learning based gait abnormality detection using Microsoft Kinect sensor

Abstract In this paper, the investigation of human gait for normal and abnormal persons is done. It involves investigating different body joints, especially the foot while walking, on heel off and heel strike stage. The heel off and heel strike comes under phases of the gait cycle of a person. In a gait cycle, several features were calculated for different persons. The designed system is continuously monitoring the person’s features and can derive comprehensive body joints information of the gait cycle. In this study, a data set is constructed. Twenty-four persons (male and female) participated, including those who have a normal and abnormal gait. The persons are instructed to walk along a straight-line path of 8 ft in length. Data is recorded using Kinect Sensor v1.0, and several features are calculated, and the analysis of features is done using MATLAB. This investigation includes creating a GUI tool for feature extraction from the Microsoft Kinect Sensor and classification of feature set for the normal and abnormal gait of a person using the basic feature extraction model. 83.33% of accuracy is achieved for identifying that the person is a normal or an abnormal during walking.