Advancing human movement evaluation using artificial intelligence
Human movement disorders affect one-third of Australians; however, conventional approaches to assessing joint motion are costly and largely clinic- or laboratory-based. This Fellowship aims to combine biomechanical modelling and advanced machine learning to non-invasively produce accurate, low-cost, user-friendly shoulder and elbow joint angle measurements using wearable inertial sensors. The technology will enable a non-expert to obtain reliable kinematics data in any location. Accurate, wearable motion measurement will benefit next-generation healthcare including telemedicine and remote rehabilitation for isolated communities, performance monitoring of elite athletes and military personnel, and the gaming and film/animation industries.
Associate Professor Ackland is Deputy Director of the ARC Training Centre for Medical Implant Technologies, and an ARC Future Fellowship. He graduated with a Bachelor of Science (Neuroscience) and Bachelor of Engineering (Mechanical), and went on to complete a PhD and postdoctoral studies in musculoskeletal biomechanics at the University of Melbourne. Associate Professor Ackland’s research focuses on computational modelling and simulation of human movement biomechanics, with a particular emphasis on design and evaluation of joint replacements for the treatment of end-stage bone and joint conditions, and applications in upper limb and maxillofacial surgery. He employs medical imaging, human motion experiments, musculoskeletal modelling, and in vitro joint-biomechanics experiments as his primary research techniques.