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

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会议 (ACGIH) 手部活动水平 (HAL) 的可行性。我们正在开发特征提取算法，用于使用专注于上肢的传统数字视频实时量化 HAL。这项研究意义重大，因为目前还没有实用的工具可以客观、不引人注目、有效地测量重复运动暴露，以评估工作场所肌肉骨骼损伤的风险。目前的方法涉及使用附在工人手或手臂上的仪器进行直接测量，或间接观察。仪器和观察方法大多仅限于研究，对于行业从业者来说非常不切实际。所提出的方法具有创新性，因为它使用数字视频处理来测量重复运动暴露，并且利用了通过与加州大学伯克利分校 (UCB) 合作手动分析的庞大视频数据库和相关暴露数据。 UCB 正在为这项初步研究提供 4 个行业工人的 320 个视频及其相关分析。概念验证算法已经经过测试。我们首先建议进一步开发和完善视频处理算法，以自动、连续地测量针对上肢的标准数字视频记录中的 HAL。然后，将通过比较重复频率、占空比和 HAL 来评估视频算法，这些重复频率、占空比和 HAL 是由我们的算法从实验室受试者执行有节奏的重复任务或实际工业工人的视频剪辑中得出的，与人类分析师进行手动逐帧操作所获得的类似结果进行比较。帧多媒体视频任务分析。最终，这项转化研究可能会产生一种基于视频的直读暴露评估工具，在职业健康和安全方面具有广泛的应用。 公共卫生相关性：上肢肌肉骨骼损伤在涉及高度重复运动和用力的手部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，使用专注于上肢的传统数字视频实时量化手部活动水平，以对工作相关疾病进行初级预防和控制。