Multiple Kinect based system to monitor and analyze key performance indicators of physical training

• Published in: Human-centric computing and information sciences Vol. 10; no. 1
• Main Authors: Ryselis, Karolis, Petkus, Tautvydas, Blažauskas, Tomas, Maskeliūnas, Rytis, Damaševičius, Robertas
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 14.12.2020; Korea Information Processing Society, Computer Software Research Group
• Subjects: Algorithms; Artificial Intelligence; Asymmetry; Coefficient of variation; Communications Engineering; Computer Science; Computer Systems Organization and Communication Networks; Data integration; Dynamic characteristics; Electronic devices; Fatigue; Human motion; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Joints (anatomy); Mathematical analysis; Networks; Physical training; Rehabilitation; Reliability analysis; Sports medicine; System reliability; Tracking devices; User Interfaces and Human Computer Interaction; Data fusion; Smart health; Physical training; Motion tracking; Kinect sensors
• DOI: 10.1186/s13673-020-00256-4
• ISSN: 2192-1962

Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.