Video Exposure Assessment of Hand Activity Level

描述（由申请人提供）：本探索性研究探讨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，用于使用传统的上肢数字视频实时量化HAL。这项研究意义重大，因为目前还没有实用的仪器来客观、不显眼、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。目前的方法包括使用工人手上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局限于研究，对行业从业者来说是非常不切实际的。所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，因为它利用了大量的视频数据库和相关的曝光数据，这些数据已经通过与加州大学伯克利分校（UCB）的合作进行了手动分析。UCB为这项初步研究提供了来自四个行业的320个工人的视频，以及他们的相关分析。一种概念验证算法已经经过了测试。我们首先建议进一步开发和完善视频处理算法，以便从上肢的标准数字视频记录中自动连续测量HAL。然后，通过比较我们的算法从执行有节奏重复任务的实验室受试者或实际工业工人的视频剪辑中得出的重复频率、占空比和HAL，与人工分析员进行手动逐帧多媒体视频任务分析获得的类似结果，对视频算法进行评估。最终，这项转化研究可能会导致基于视频的直接阅读暴露评估工具，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. 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.