Video Exposure Assessment of Hand Activity Level

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师会议(ACGIH)手活动水平(HAL)的可行性。我们正在开发使用专注于上肢的传统数字视频实时量化HAL的特征提取算法。这项研究具有重要意义，因为目前还没有一种实用的工具来客观、不引人注目地、有效地测量重复运动暴露来评估工作场所肌肉骨骼损伤的风险。目前的方法要么是使用连接在工人手或手臂上的仪器进行直接测量，要么是间接观察。仪器和观察方法大多局限于研究性研究，对行业从业者来说非常不切实际。提出的方法是创新的，因为它使用数字视频处理来测量重复运动曝光，并且它利用了已通过与加州大学伯克利分校(UCB)合作手动分析的视频和相关曝光数据的海量数据库。UCB将为这项初步研究提供四个行业工人的320个视频及其相关分析。一个概念验证算法已经过测试。我们首先建议进一步开发和改进视频处理算法，以便从专注于上肢的标准数字视频记录中自动和连续地测量HAL。然后，将通过将我们的算法从执行有节奏的重复任务的实验室受试者或实际产业工人的视频剪辑中得出的重复频率、占空比和HAL与由执行手动逐帧多媒体视频任务分析的人类分析师获得的类似结果进行比较来评估视频算法。最终，这项翻译研究可能导致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。

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.