A Direct Reading Video Assessment Instrument for Repetitive Motion Stress

项目摘要/摘要 这项研究研究了计算机视觉是否可以更有效地评估工人暴露和评估 与传统方法相比，工伤的相关风险更高。目前的方法涉及到 用戴在工人手上或胳膊上的仪器进行观察或测量。据观察， 经常被认为太主观或不准确，仪器太具侵入性或太耗时而无法进行常规操作 在工业上的应用。自动化工作分析可能会提供更客观、准确、可重复的 以及比观察性分析更有效的暴露评估工具。计算机视觉使用更少的资源 比工人身上的仪器更好，不会干扰生产；可以量化更多的接触 变量和交互作用；适用于长期、直接阅读暴露评估；并提供 与视频同步的动画数据可视化，用于识别需要的作业的各个方面 干预措施。本研究从多机构协同的角度对研究进行了杠杆分析 2001至2010年间进行的与上肢工作相关的MSD研究，研究了生产和 来自美国不同行业的服务人员，并使用严格的案例标准和个人级别 前瞻性的暴露评估，包括录制工作的详细视频。我们的学习伙伴 来自华盛顿州劳动与工业局的国家职业安全与健康研究所 安全与健康评估与预防研究计划和加州大学旧金山分校 Francisco将提供任务级别的视频、相关的暴露变量数据和预期健康 1,649名工人的结果。直接从工作视频和 来自前瞻性研究数据库的相应健康结果将建立剂量反应 关系转化为一个原型的自动化工作分析工具。我们建立在以前的基础上 成功开发用于估计ACGIH手活动的视频无标记手部运动算法 高水平、可靠的视频处理方法，用于在具有挑战性的观看条件下进行手部跟踪。这 提案将完善和开发额外的视频算法，并分析视频以提取曝光 对重复、姿势、用力及其相互作用的测量。视频提取曝光 这些措施将与我们制定的常规观测暴露措施进行比较。 合作者。视频和相应的观测数据将与预期健康相结合 用于评估剂量反应以及开发和验证节俭暴露风险的结果数据 自动直读式重复运动仪的型号。我们将测试自动化是否更好 预测能力优于观察，还考虑了计算机视觉的准确性和实用性 针对选定行业工作的传统工作分析进行分析。这项建议解决了 NIOSH在肌肉骨骼疾病以及暴露评估方面的跨部门计划。这 翻译研究与研究到实践(R2P)的倡议是一致的，通过开发 传播NIOSH最近赞助的关于MSD的前瞻性研究的知识的技术。

Project Summary/ Abstract This research studies if computer vision can more effectively evaluate worker exposure and assess the associated risk for work related injuries than conventional methods. Current methods involve either observations or measurements using instruments attached to a worker's hands or arms. Observation is often considered too subjective or inaccurate, and instruments too invasive or time consuming for routine applications in industry. Automated job analysis potentially offers a more objective, accurate, repeatable, and efficient exposure assessment tool than observational analysis. Computer vision uses less resources than instruments attached to workers and does not interfere with production; can quantify more exposure variables and interactions; is suitable for long-term, direct reading exposure assessment; and offers animated data visualizations synchronized with video for identifying aspects of jobs needing interventions. This research leverages the research from coordinated multi-institutional prospective studies of upper limb work related MSD conducted between 2001 and 2010 that studied production and service workers from a variety of US industries, and used rigorous case-criteria and individual-level exposure assessments prospectively, including recording detailed videos of the work. Our study partners from the National Institute for Occupational Safety and Health, the Washington State Labor & Industries Safety & Health Assessment & Research for Prevention program and the University of California-San Francisco will provide task-level videos, associated exposure variable data, and prospective health outcomes for 1,649 workers. Exposure properties directly measured from videos of jobs and corresponding health outcomes from the prospective study database will establish dose-response relationships to translate into a prototype automated job analysis instrument. We build on our previous success in developing video marker-less hand motion algorithms for estimating the ACGIH hand activity level, and reliable video processing methods for hand tracking under challenging viewing conditions. This proposal will refine and develop additional video algorithms, and analyze the videos to extract exposure measures for repetition, posture, exertions, and their interactions. The video extracted exposure measures will be compared against conventional observational exposure measures made by our collaborators. Video and corresponding observational data will be merged with the prospective health outcomes data to evaluate dose-response and to develop and validate parsimonious exposure risk models for an automated direct reading repetitive motion instrument. We will test if automation has better predictive capability than observation and also consider the accuracy and utility of computer vision analysis against conventional job analysis for selected industrial jobs. This proposal addresses the NIOSH cross-sector programs in Musculoskeletal Disorders as well as in Exposure Assessment. This translational research is in concurrence with the Research to Practice (r2P) initiative by developing technology to disseminate knowledge from recent NIOSH sponsored prospective studies on MSDs.