I-Corps: Automated Postures Analysis for Ergonomic Risk

制造业、建筑业、零售业、医疗保健业和物流业等行业的工人都参与体力要求高的活动，处理笨拙的身体姿势和重复的手动处理任务，这些任务会导致人体工程学伤害（例如，肌肉骨骼疾病）。 例如，在美国，人体工程学伤害平均占非致命职业伤害和疾病的33%，这些伤害也与高成本有关（美国每年约500亿美元）。由于缺勤、生产力下降以及医疗保健、残疾和工人赔偿费用的增加，为了处理这种伤害，基于人工观察的人体工程学评估（例如，检查表）已被广泛用于识别笨拙或/和重复的工作姿势。然而，人工观测方法耗时、昂贵且容易出错，这使得它们难以容易地应用于许多工作场所。对训练有素的分析师的需求也是促进工作场所人体工程学评估的一个障碍。因此，需要一种有效且易于使用的人体工程学评估方法，以及时检测并最大限度地减少人体工程学伤害的风险。所提出的基于计算机视觉的自动姿势分析方法处理通过普通视频记录设备（如智能手机、平板电脑和现成的摄像机）拍摄的工人的视频图像，从而评估他们在执行工作场所任务时的人体工程学风险水平。拟议的创新是通过最大限度地减少耗时、昂贵和容易出错的手动观察，使当前的人体工程学风险评估过程高效、经济和可靠。工作姿势的图像序列具有可区分的模式，其可用于区分安全姿势和易受伤姿势。通过学习这些模式，这项技术可以自动识别视频记录中的尴尬姿势，使人们能够及时进行人体工程学评估，而无需技术复杂性或技能。拟议的技术方法也足够灵活和强大，足以应对复杂和拥挤的工作环境。特别是，这种创新的虚拟建模方法可以自动创建大量的训练数据集，消除了繁琐的数据收集。此外，区分不同的姿态和实现快速姿态估计与移动的设备的能力，使所提出的方法可以应用到不同的人体工程学检查表在许多行业。这项创新可以为许多遭受人体工程学伤害的行业提供一条令人兴奋的道路，以减轻他们的人工观察负担，最终为预防人体工程学伤害和提高生产力打开了一扇大门。

Workers in industries like manufacturing, construction, retail, health care, and logistics are involved in physically demanding activities, dealing with awkward body postures and repetitive manual handling tasks that result in ergonomic injuries (e.g., musculoskeletal disorders). For example, ergonomic injuries account for an average of 33% of nonfatal occupational injuries and illnesses in the U.S. These injuries are also associated with high costs (about $50 billion annually in the U.S.) to employers due to absenteeism, lost productivity, and increased health care, disability, and workers' compensation costs. To deal with such injuries, manual observation-based ergonomic assessments (e.g., checklists) have been widely used to identify awkward or/and repetitive working postures. However, manual observation methods are time-consuming, expensive and error prone, which makes them difficult to be easily applied to many workplaces. The need for trained analysts is also an obstacle to promote ergonomic assessments in workplaces. As a result, an effective and easily accessible means for ergonomic assessments is required to detect and minimize the risks of ergonomic injuries in a timely-manner. The proposed computer vision-based automatic posture analysis approach processes video images of workers taken via ordinary video recording devices like smartphones, tablets, and off-the-shelf camcorders, and consequently evaluates the level of their ergonomic risk they have while performing workplace tasks. The proposed innovation is to make the current ergonomic risk assessment process efficient, affordable, and robust by minimizing time-consuming, expensive, and error prone manual observation. Image sequences of working postures have distinguishable patterns that can be used to differentiate safe and injury-prone postures. By learning these patterns, this technology automatically identifies awkward postures on video recordings, enabling one to conduct ergonomic assessments in a timely manner without technical sophistication or skill. The proposed technical approach is also flexible and robust enough to deal with complex and crowded work environments. Particularly, this innovative virtual modeling approach to automatically create massive training datasets eliminates cumbersome data collection. In addition, the capability to differentiate different postures and realize rapid pose estimation with mobile devices enables the proposed approach to be applied to diverse ergonomic checklists in many industries. This innovation can provide an exciting path for many industries who suffer ergonomic injuries to reduce their burden of manual observation, ultimately opening a door toward the prevention of ergonomic injuries and the increase of productivity.