Determining the Quality of Human Movement using Kinect Data

• Main Authors: Thati, Satish Kumar, Mareedu, Venkata Praneeth
• Format: Others
• Language: English
• Published: Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling 2017
• Subjects: 3D cameras; Microsoft Kinect; Rehabilitation; Depth Tracking; Skeleton Tracking; Healthcare.
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13819

Health is one of the most important elements in every individual’s life. Even though there is much advancement in science, the quality of healthcare has never been up to the mark. This appears to be true especially in the field of Physiotherapy. Physiotherapy is the analysis of human joints and bodies and providing remedies for any pains or injuries that might have affected the physiology of a body. To give patients a top notch quality health analysis and treatment, either the number of doctors should increase, or there should be an alternative replacement for a doctor. Our Master Thesis is aimed at developing a prototype which can aid in providing healthcare of high standards to the millions.  Methods: Microsoft Kinect SDK 2.0 is used to develop the prototype. The study shows that Kinect can be used both as Marker-based and Marker less systems for tracking human motion. The degree angles formed from the motion of five joints namely shoulder, elbow, hip, knee and ankle were calculated. The device has infrared, depth and colour sensors in it. Depth data is used to identify the parts of the human body using pixel intensity information and the located parts are mapped onto RGB colour frame.  The image resulting from the Kinect skeleton mode was considered as the images resulting from the markerless system and used to calculate the angle of the same joints. In this project, data generated from the movement tracking algorithm for Posture Side and Deep Squat Side movements are collected and stored for further evaluation.  Results: Based on the data collected, our system automatically evaluates the quality of movement performed by the user. The system detected problems in static posture and Deep squat based on the feedback on our system by Physiotherapist.