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.

项目总结/摘要 这项研究研究研究了计算机视觉是否可以更有效地评估工人的暴露情况， 与传统方法相比，目前的方法包括 用固定在工人手上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，并且仪器对于常规来说过于侵入性或耗时 在工业上的应用。自动化的工作分析可能提供一个更客观，准确，可重复， 和有效的暴露评估工具。计算机视觉使用更少的资源 不影响生产;可以量化更多的接触 变量和相互作用;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 干预措施。本研究从多机构协调的角度进行研究， 2001年至2010年期间进行的与MSD相关的上肢工作研究， 来自美国各行各业的服务业工人，并使用严格的案例标准和个人水平 前瞻性的暴露评估，包括记录工作的详细视频。我们的学习伙伴 来自国家职业安全与健康研究所、华盛顿州劳工和工业部 安全与健康评估与预防研究项目和加州大学旧金山分校 弗朗西斯科将提供任务级视频、相关暴露变量数据和预期健康 为1,649名工人提供服务。直接从工作视频中测量的曝光特性， 来自前瞻性研究数据库的相应健康结果将建立剂量反应 将这些关系转化为原型自动化工作分析工具。我们建立在我们以前的 成功开发视频无标记手部运动算法，用于估计ACGIH手部活动 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率 测量重复、姿势、用力及其相互作用。视频提取曝光 我们会将这些措施与我们的常规观测暴露措施进行比较， 合作者视频和相应的观察数据将与前瞻性健康 结果数据，以评价剂量反应，并制定和验证简约的暴露风险 自动直接阅读重复运动仪器的模型。我们将测试自动化是否有更好的 预测能力比观察，还考虑计算机视觉的准确性和实用性 对选定的工业工作进行常规工作分析。该提案针对 NIOSH在肌肉骨骼疾病以及暴露评估方面的跨部门计划。这 翻译研究与研究实践（r2 P）倡议是一致的， 技术传播知识，从最近NIOSH赞助的前瞻性研究MSD。