Using computer vision and deep learning to measure worker kinematics

项目概要/摘要 肌肉骨骼疾病（MSD）是最常见和最昂贵的非致命性工伤， 几乎所有美国工业部门的疾病。响应NIOSH中强调的明确需求 国家肌肉骨骼健康研究议程，以制定估计暴露于 职业风险因素的MSD，这项研究将验证新的软件测量工人的姿势， 仅使用标准视频作为输入的运动。该软件利用了计算机视觉的重大进展 和机器学习科学，直到最近才能在三个方面测量人类姿势， 使用标准的二维视频或图像源在三维空间中显示图像。最终，我们的长期目标之一 目标是为职业安全和健康从业人员开发类似于广泛使用的直接 用于评估职业危害暴露的阅读仪器（例如，声压计和气体 监视器）。在这个最初的R21中，我们建议验证我们的软件产生的姿势数据（目标1）， 检查由我们软件输出的姿势信息与更传统的输出之间的一致性 (but耗时）基于观察的视频分析（目标2）。在目标1中，参与者将执行重复的， 手臂密集型任务，涉及达到和操纵安装在位于前面的夹具旋钮 身体然后，我们将估计颈部、肩部、肘部、手腕、躯干和膝盖角度的准确性 位移（即，与同时收集的数据相比， 使用光学动作捕捉系统。实验变量包括所需的运动范围， 参与者执行任务和摄像机的配置，用于记录参与者的视频， 任务。目标1的结果将提供有关我们所需新软件性能的关键信息 提供最佳实践信息，以便在能够进行现场暴露评估的应用中实施。在目标2中， 将重新分析在以前的一项前瞻性研究中获得的1000多个工作场所视频。 制造业工人中的极端MSD。建议进行分析以评估方法间一致性 自动视频分析（我们的软件）和经过培训的专业观察员完成的分析之间的差异 在前瞻性研究的过程中。结果将提供证据，证明我们的软件可以量化 在进行常用观察所需的一小部分时间内职业暴露于MSD风险因素- 基于分析。对现有工作场所视频的重新分析也可以开辟新的探索途径 职业暴露于默沙东危险因素与未来事件健康结果之间的关系 问题研究

PROJECT SUMMARY/ABSTRACT Musculoskeletal disorders (MSDs) are among the most frequent and costly nonfatal work-related injuries and illnesses across virtually all US industry sectors. Responding to the clear need emphasized in the NIOSH National Research Agenda for Musculoskeletal Health to develop improved methods of estimating exposure to occupational risk factors for MSDs, this research will validate new software for measuring worker postures and movements using only standard video as input. The software leverages major advances in computer vision and machine learning sciences that only recently have enabled measurement of human postures in three dimensional space using standard two dimensional video or image sources. Ultimately, one of our long-term goals is to develop applications for occupational safety and health practitioners analogous to widely-used direct reading instruments for assessing exposure to occupational hazards (e.g., sound pressure meters and gas monitors). In this initial R21, we propose to validate the postural data our software produces (Aim 1) and examine agreement between postural information output by our software and that output by more traditional (but time-consuming) observation-based video analyses (Aim 2). In Aim 1, participants will perform a repetitive, arm-intensive task involving reaching to and manipulating knobs mounted to a fixture located in front of the body. We will then estimate the accuracy of neck, shoulder, elbow, wrist, trunk, and knee angular displacements (i.e., posture over time) measured by our software, compared to data simultaneously collected using an optical motion capture system. Experimental variables include the range of motion required of participants to perform the task and the configuration of the camera used to record video of participants during the task. Results from Aim 1 will provide critical information about the performance of our new software needed to inform best-practices for implementation in field-capable exposure assessment applications. In Aim 2, we will reanalyze >1000 workplace videos obtained during the course of a previous prospective study of upper extremity MSDs among manufacturing workers. Analyses are proposed to assess the inter-method agreement between automated video analyses (our software) and analyses completed by trained specialist observers during the course of the prospective study. Results will provide evidence that our software can quantify occupational exposure to MSD risk factors at a fraction of time needed to perform commonly used observation- based analyses. The reanalysis of existing workplace videos can also open new pathways to explore associations between occupational exposures to MSD risk factors and incident health outcomes in future studies.