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.

项目摘要/摘要 该研究研究计算机视觉是否可以更有效地评估工人的接触并评估 与传统方法相比，与工作相关伤害的相关风险。当前方法涉及 使用工具或手臂上的工具进行观察或测量。观察是 通常被认为太主观或不准确，仪器过于侵入性或耗时 行业的应用。自动化分析可能会提供更客观，准确，可重复的， 与观察分析相比，有效的暴露评估工具。计算机视觉使用的资源较少 比工人附属的仪器不干扰生产；可以量化更多的曝光 变量和互动；适用于长期直接阅读暴露评估；和优惠 动画数据可视化与视频同步以识别需要的作业方面 干预措施。这项研究利用了协调的多机构的前瞻性研究 在2001年至2010年之间进行的有关上肢工作相关的上肢工作的研究研究了生产和 来自美国各种行业的服务人员，并使用了严格的病例标准和个人级别 前瞻性评估，包括记录工作的详细视频。我们的学习伙伴 华盛顿州劳动与工业国家职业安全与健康研究所 安全与健康评估与预防计划和加利福尼亚大学的研究 弗朗西斯科（Francisco）将提供任务级视频，相关的曝光变量数据和预期健康 1,649名工人的成果。直接从工作视频和 前瞻性研究数据库的相应健康结果将建立剂量反应 转化为原型自动化工作分析工具的关系。我们以先前的 成功开发无视频标记的手运动算法来估算ACGIH手术 在挑战性的观看条件下，用于手工跟踪的可靠视频处理方法。这 建议将完善和开发其他视频算法，并分析视频以提取曝光 重复，姿势，努力及其相互作用的措施。视频提取了曝光 将措施与我们的常规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合并 结果数据以评估剂量响应并发展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更好 预测能力比观察能力，还考虑计算机视觉的准确性和实用性 针对选定工业工作的常规工作分析分析。该提议解决了 NIOSH肌肉骨骼疾病以及暴露评估中的跨部门计划。这 转化研究与研究（R2P）倡议是一致的 从NIOSH最近赞助的有关MSD的前瞻性研究中传播知识的技术。