Non-invasive and safe human-machine interface (HMI) systems . This project aims to establish novel non-invasive human-machine interface systems based on multi-modal sensing and machine learning to intuitively command and control robotic and autonomous systems safely interacting and cooperating with humans. This will be achieved by harnessing the synergies across design optimisation, multi-modal sensing, additive manufacturing, machine learning, and assistive and cooperative robotic devices. Expe ....Non-invasive and safe human-machine interface (HMI) systems . This project aims to establish novel non-invasive human-machine interface systems based on multi-modal sensing and machine learning to intuitively command and control robotic and autonomous systems safely interacting and cooperating with humans. This will be achieved by harnessing the synergies across design optimisation, multi-modal sensing, additive manufacturing, machine learning, and assistive and cooperative robotic devices. Expected outcomes are a novel human-machine interface methodology, a new multi-purpose wearable data glove, and function and application-specific machine learning methods for cutting-edge applications in assistive robotic devices such as a prosthetic hand, advanced manufacturing, construction and agriculture.Read moreRead less
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 project 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 o ....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 project 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.Read moreRead less