Video Exposure Assessment of Hand Activity Level

手部活动水平的视频暴露评估

• 批准号: 8237418
• 负责人: ROBERT G RADWIN
• 金额: $ 25.53万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8237418
• 关键词: Video; Exposure; Assessment; Hand; Activity; Video; Exposure; Assessment; Hand; Activity

DESCRIPTION (provided by applicant): This exploratory research investigates the feasibility of automatically evaluating the American Conference of Government Industrial Hygienists (ACGIH) Hand Activity Level (HAL) using digital video processing. We are developing feature extraction algorithms for quantifying HAL in real-time using conventional digital videos focused on the upper extremities. This research is significant because there is currently no practical instrument for objectively, unobtrusively, and efficiently measuring repetitive motion exposure for evaluating the risk of musculoskeletal injuries in the workplace. Current methods involve either direct measurements using instruments attached to a worker's hands or arms, or indirect observations. Both instrument and observation methods are mostly limited to research studies and are highly impractical for industry practitioners. The proposed approach is innovative because it uses digital video processing to measure repetitive motion exposure and because it leverages a vast data base of videos and associated exposure data already analyzed manually through collaboration with the University of California-Berkeley (UCB). UCB is making available 320 videos of workers in four industries, and their associated, analyses for this initial study. A proof-of-concept algorithm has already been tested. We first propose to further develop and refine video processing algorithms for automatically and continuously measuring HAL from standard digital video recordings focused on the upper extremities. The video algorithms will then be evaluated by comparing repetition frequency, duty cycle and HAL derived by our algorithm from video clips of laboratory subjects performing paced repetitive tasks or actual industrial workers, against similar outcomes obtained by a human analyst conducting a manual frame-by-frame multimedia video task analysis. Ultimately this translational research might lead to a video-based direct reading exposure assessment instrument with broad applications for occupational health and safety. PUBLIC HEALTH RELEVANCE: Upper extremity musculoskeletal injuries are common in hand intensive work involving highly repetitive motions and exertions. This exploratory research will develop video processing algorithms to quantify hand activity level in real-time using conventional digital videos focused on the upper extremities for primary prevention and control of work related disorders.

描述（由申请人提供）：这项探索性研究调查了使用数字视频处理自动评估美国政府工业卫生学家（ACGIH）手活动水平（HAL）的可行性。我们正在开发功能提取算法，用于使用关注上肢的常规数字视频实时量化HAL。这项研究很重要，因为目前没有实用的工具可以客观，毫不客气，有效地测量重复性运动暴露，以评估工作场所肌肉骨骼损伤的风险。当前方法涉及使用工具或手臂上的仪器或间接观察的仪器进行直接测量。仪器和观察方法都大多限于研究，对于行业从业者来说是非常不切实际的。提出的方法具有创新性，因为它使用数字视频处理来衡量重复运动的曝光，并且由于它通过与加利福尼亚大学 - 伯克利大学（UCB）的合作来利用已经手动分析的视频和相关曝光数据的大量数据库。 UCB在四个行业中提供了320个工人的320个视频，并对这项初步研究的分析进行了分析。概念验证算法已经进行了测试。我们首先建议进一步开发和完善视频处理算法，以自动，不断地测量关注上肢的标准数字视频记录。然后，将通过比较我们的算法的重复频率，占空比和HAL来评估视频算法，该算法是从执行节奏重复任务或实际工业工人的实验室受试者的视频片段中得出的，与进行手动帧帧多帧视频视频任务分析的人类分析师获得的类似结果。最终，这项翻译研究可能会导致基于视频的直接阅读曝光评估工具，并具有广泛的职业健康和安全应用。 公共卫生相关性：上肢肌肉骨骼损伤在涉及高度重复性动作和劳累的手工密集型工作中很常见。这项探索性研究将开发视频处理算法，以实时量化手动活动水平，使用的传统数字视频着重于上肢，用于初级预防和控制与工作有关的疾病。