| Lecturer Profile |
Dr. Zhi-Qiang Zhang received the B.E. degree in computer science and technology from Tianjian University, China, in 2005, and the Ph.D. degree in 2010 in electrical engineering from the Sensor Network and Application Research Center, Graduate University, Chinese Academy of Sciences, where he led the research and development of human motion capture using micro-sensors and the ubiquitous dynamic heart monitor. Both of the developed MMocap system and the heart monitor have been commercialized in Microsens Inc. Wuxi, China. After he finished his PhD, he used to work as a Research Associate in the Department of Computing, Imperial College London, leading the research of biomtion analysis and its transnational healthcare applications in the Hamlyn centre for robotic surgery and as a Marie Cuire Fellow in CETEMMSA, working on printed strain sensor base motion estimation. He is going to take a faculty position in the coming March in University of Leeds. His research interests include Body Sensor Network, Information Fusion and Machine Learning. |
| Lecture Abstract |
The human motion capture technology has been used to analyse the biomechanics of patients, and the analysis provides an objective measure of physical function to aid interventional planning, evaluate the outcomes of surgical procedures, and assess the efficacy of treatment and rehabilitation. In contrast to traditional vision based motion tracking systems, inertial motion capture is based on miniature inertial sensors combined with biomechanical models and sensor fusion algorithms, which offers much greater flexibility without any spatial constraints. However, for practical use of wearable inertial-sensor based tracking systems, there are still many technical hurdles related to the handling of sensor drift, errors due to sensor placement, and accurate orientation estimation and motion reconstruction. In this talk, I will cover the basic sensor fusion theory and methodology for human motion capture using wearable inertial/magnetic sensors, including micro-sensor calibration, Bayesian sensor fusion for orientation estimation, linear acceleration interference and magnetic disturbance detection and processing scheme, and biomechanical-model based body Centre-of-Mass displacement estimation tactics. The application of the system for the human upper limb rehabilitation will also be discussed. |
