Discovery of Gait Anomalies from Motion Sensor Data

A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms in order to identify the specific gait anomaly. We designed medically oriented features for training a machine learning classifier that classifies the user´s gait into: i) normal, ii) with hemiplegia, iii) with Parkinson´s disease, iv) with pain in the back and v) with pain in the leg. Experimental results show that the proposed tool is usable for discovery of gait anomalies.