A Direct Reading Video Assessment Instrument for Repetitive Motion Stress

直读视频重复运动压力评估仪

• 批准号: 9357554
• 负责人: ROBERT G RADWIN
• 金额: $ 45.22万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357554
• 关键词: Direct; Reading; Video; Assessment; Instrument

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.

项目概要/摘要 这项研究研究计算机视觉是否可以更有效地评估工人的暴露程度并评估 工伤相关风险高于传统方法。当前的方法涉及 使用安装在工人手或手臂上的仪器进行观察或测量。观察是 通常被认为过于主观或不准确，并且仪器对于日常工作来说过于侵入性或耗时 在工业中的应用。自动化工作分析可能提供更客观、准确、可重复、 比观察分析更有效的暴露评估工具。计算机视觉使用更少的资源 比工人随身携带的工具更不干扰生产；可以量化更多的曝光 变量和相互作用；适合长期、直读暴露评估；和优惠 与视频同步的动画数据可视化，用于识别工作需要的各个方面 干预措施。这项研究利用了协调的多机构视角的研究 2001 年至 2010 年间进行的上肢工作相关 MSD 研究，研究了生产和 来自美国各个行业的服务人员，并使用严格的案例标准和个人层面 前瞻性暴露评估，包括录制工作的详细视频。我们的学习伙伴 来自国家职业安全与健康研究所、华盛顿州劳工与工业局 安全与健康评估与预防研究计划和加州大学圣分校 Francisco 将提供任务级视频、相关暴露变量数据和预期健康状况 1,649 名工人的成果。直接根据作业视频测量的曝光特性 前瞻性研究数据库中相应的健康结果将建立剂量反应 关系转化为自动化工作分析工具原型。我们以之前的基础为基础 成功开发用于估计 ACGIH 手部活动的无视频标记手部运动算法 水平和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 该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 重复、姿势、努力及其相互作用的测量。视频提取曝光 措施将与我们制定的传统观察暴露措施进行比较 合作者。视频和相应的观察数据将与预期健康状况合并 用于评估剂量反应并开发和验证简约暴露风险的结果数据 自动直读重复运动仪器的模型。我们将测试自动化是否更好 预测能力比观察还要考虑计算机视觉的准确性和实用性 针对选定的工业职位的传统职位分析进行分析。该提案解决了 NIOSH 肌肉骨骼疾病和暴露评估的跨部门项目。这 转化研究与研究到实践（r2P）倡议相一致，通过开发 传播 NIOSH 最近资助的 MSD 前瞻性研究知识的技术。