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Field of Research : Biomechanical Engineering
Field of Research : Biomedical Instrumentation
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  • Funded Activity

    Linkage Projects - Grant ID: LP150100905

    Funder
    Australian Research Council
    Funding Amount
    $430,000.00
    Summary
    Intelligent training (iTraining) for the human Achilles tendon. The project aims to improve understanding of the mechanical environment of the Achilles tendon. The Achilles tendon plays a crucial role in human motor function and is also a structure that is commonly injured and notoriously difficult to treat. A major barrier to improving Achilles tendon function, preventing tendon injury and enhancing tendon repair is a poor understanding of the mechanical environment of the Achilles tendon durin .... Intelligent training (iTraining) for the human Achilles tendon. The project aims to improve understanding of the mechanical environment of the Achilles tendon. The Achilles tendon plays a crucial role in human motor function and is also a structure that is commonly injured and notoriously difficult to treat. A major barrier to improving Achilles tendon function, preventing tendon injury and enhancing tendon repair is a poor understanding of the mechanical environment of the Achilles tendon during training and rehabilitation. The project aims to develop a better understanding of the loading conditions that optimise tendon metabolism. Based on this, it then intends to develop new technologies to estimate the mechanical behaviour of the human Achilles tendon in real time based on integrated use of wearable technology, and new training guidelines that will optimise human tendon adaptation.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210102911

    Funder
    Australian Research Council
    Funding Amount
    $334,055.00
    Summary
    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.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT200100098

    Funder
    Australian Research Council
    Funding Amount
    $920,059.00
    Summary
    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.
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    Showing 1-3 of 3 Funded Activites

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