Wearable technology to inform fatigue and injury processes with ergonomic applications

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The research outlined herein will advance the areas of ergonomics, computational modelling, upper extremity biomechanics, aetiology of repetitive strain injuries, and wearable technology. Biomechanical data including wrist kinematics and fingertip forces will be collected over long time frames (entire shifts) in a workplace study.  These large datasets will be examined using machine learning and classification techniques to look for generalized trends (ie., detection of fatigue leading to injury).  This data will also inform inverse dynamics modelling, followed by simulated in-vitro testing, high resolution computed tomography images and computational modelling.  The results of this research are expected to inform biomechanical evaluation of injury risk. In addition, the proposed research plan will produce a public repository of large data sets of upper extremity (wrist) kinematics, as well as high resolution, three dimensional, dynamic, computed tomography images of cadaveric wrists simulated through relevant motions using a custom CT compatible joint simulator.  Contrast agents will be used in the cadaveric wrists to visualize both bony and soft tissue structures.  Muscle loads, calculated through inverse dynamics calculations, will inform the simulation of workplace movements and loading scenarios. The resulting images will then be used to develop computational models that will predict tissue stresses and strains.  These models will be validated by experimental measure of in-situ soft tissue strain using digital image correlation methods.  The entire research plan will enable informed and validated prediction of injury risk, using data from wearable devices that can easily be incorporated in the workplace.  In addition, the research will serve to close several gaps, including linking wearable data to validated biomechanical analysis; and providing sound validation data to commonly used modelling techniques.  Finally, the application of machine learning algorithms is anticipated to yield new discovery around fatigue, injury prevention, and biomechanical risk calculations.