PFI: AIR - TT: Vision-based ergonomic risk assessment of working postures

这个PFI：空气技术翻译项目专注于翻译基于计算机视觉的姿势分类和姿势估计方面的基础研究成果，以满足快速和简单的人体工学姿势风险评估的需求。这适用于从事体力要求高、工作姿势尴尬的职业工人，他们因此患有与工作相关的肌肉骨骼疾病(WMSD)。翻译后的技术很重要，因为它使从业者能够在实际伤害之前识别潜在的WMSD风险，从而将潜在的伤害风险降至最低。鉴于美国大规模的WMSD病例(例如，每年超过360,000例)和巨额的相关工人补偿成本(例如，每年超过142亿美元)，这项研究有助于发展和维护一支安全、健康和有生产力的劳动力队伍，这将对社会产生改善影响。该项目将产生一个基于视觉的人体工学风险评估的全功能软件原型，它具有以下独特特征：快速(例如，接近实时地处理视频帧)、容易(例如，不需要培训来评估人体工学风险)和自动化(例如，不需要人工风险评估)。与目前人体工程学行业中基于观察的手动风险评估相比，这些功能允许从业者在人体工程学风险评估上节省大量时间和精力。该项目在从研究发现转化为商业应用的过程中解决了以下技术差距。首先，基于跟踪的姿态估计是估计姿态严重性的基本组成部分，它提供了对视频帧的快速处理，但需要在不丢失目标的情况下保持健壮性，这可能是由于实际工作场所频繁的遮挡和环境噪声造成的。在这项研究中，基于跟踪的方法将与基于检测的姿态估计相结合，并从刚刚处理的特征中采用不同的特征，如空间信息和参考帧。一旦原型被开发出来，它将在不同的实际工作地点进行测试，如制造装配线，以确保其健壮性和可用性。此外，参与该项目的人员，包括研究生和本科生，将通过与人体工程学和商业专家以及潜在客户的互动(例如，客户互动、市场研究和实地验证)以及参与密歇根大学创业中心提供的活动(例如，创业课程和研讨会)，获得创业经验。该项目聘请了北美最大的人体工程学咨询公司Humantech、UAW(美国汽车、航空航天和农业机械工人联合会)和一家风险投资家，提供真实世界的测试环境，并指导这项技术转化工作中的商业化方面从研究成果转化为商业现实。

This PFI: AIR Technology Translation project focuses on translating fundamental research findings in computer vision-based posture classification and pose estimation to fill the need for rapid and easy postural ergonomic risk assessment. This is applicable for occupational workers involved in physically demanding labor with awkward working postures, and who consequently suffer from work-related musculoskeletal disorders (WMSDs). The translated technology is important because it enables practitioners to identify potential WMSD risks in advance of actual injury, thus minimizing the potential injury risks. Given the massive number of WMSD cases (e.g., over 360,000 cases per year) and huge associated workers' compensation costs (e.g., over $14.2 billion per year) in the U.S., this research contributes to the development and maintenance of a safe, healthy, and productive workforce, which will have an ameliorative impact on society. The project will result in a fully-functioning software prototype of vision-based ergonomic risk assessment, which has the following unique features: rapid (e.g., near real-time to process video frames), easy (e.g., no training to assess ergonomic risks is needed), and automated (e.g., no need for manual risk assessment). These features allow practitioners to save a significant amount of time and effort on ergonomic risk assessment, when compared to the current manual observation-based risk assessment in the ergonomic industry. This project addresses the following technology gaps as it translates from research discovery toward commercial application. First, a tracking-based pose estimation, which is a fundamental component to estimate posture severity, provides fast processing of video frames but needs to be robust without target loss, which can happen due to frequent occlusions and environmental noises in real worksites. In this research, the tracking-based approach will be integrated with a detection-based pose estimation along with the adoptions of different features like spatial information and reference frames from just processed ones. Once the prototype is developed, it will be tested in diverse real worksites such as manufacturing assembly lines to ensure its robustness and usability. In addition, personnel involved in this project, including graduate and undergraduate students, will receive entrepreneurship experience through interaction with ergonomic and business experts and potential customers (e.g., customer interaction, market study, and field validation) as well as engagement in the activities offered from the University of Michigan Center for Entrepreneurship (e.g., entrepreneurship classes and workshops). The project engages Humantech, the largest ergonomic consulting firm in North America, UAW (United Automobile, Aerospace and Agricultural Implement workers of America), and a venture capitalist, to provide real-world test environment and guide commercialization aspects in this technology translation effort from research findings toward commercial reality.