Wearable technology to inform fatigue and injury processes with ergonomic applications

本文概述的研究将推进人体工程学，计算建模，上肢生物力学，重复性劳损的病因学和可穿戴技术领域。在工作场所研究中，将在长时间范围内（整个班次）收集生物力学数据，包括手腕运动学和指尖力。 这些大型数据集将使用机器学习和分类技术进行检查，以寻找广义趋势（即，疲劳导致受伤的检测）。 这些数据还将为逆动力学建模提供信息，然后是模拟体外测试、高分辨率计算机断层扫描图像和计算建模。 这项研究的结果有望为损伤风险的生物力学评估提供信息。此外，拟议的研究计划将产生一个上肢（手腕）运动学的大型数据集的公共存储库，以及使用自定义CT兼容关节模拟器通过相关运动模拟的尸体手腕的高分辨率、三维、动态、计算机断层扫描图像。 将在尸体手腕中使用造影剂以使骨和软组织结构可视化。 通过逆动力学计算计算的肌肉负荷将为工作场所运动和负荷场景的模拟提供信息。由此产生的图像将用于开发预测组织应力和应变的计算模型。 这些模型将通过使用数字图像相关方法的原位软组织应变的实验测量来验证。 整个研究计划将利用可穿戴设备的数据，对受伤风险进行明智和有效的预测，这些数据可以很容易地融入工作场所。 此外，该研究将有助于缩小几个差距，包括将可穿戴数据与经验证的生物力学分析联系起来;并为常用的建模技术提供可靠的验证数据。 最后，机器学习算法的应用有望在疲劳、损伤预防和生物力学风险计算方面产生新的发现。

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.