Reliability Modeling of Shoulder Fatigue and Recovery for Warehouse Operators Performing Dynamic Tasks

项目摘要 对电子商务日益增长的需求导致了仓库和配送中心的增加，沿着 有足够的劳动力来运作为了提高效率，公司正在转向零部件到 用于订单履行的人工系统，以达到每个工人每小时近500件物品的生产力水平。这些系统 创建高度重复且主要涉及手臂和肩膀的手动订单拣选作业。重复 长时间的手臂运动，如果没有足够的休息，会导致疲劳和不适 对于肩膀，这可能导致肌肉骨骼疾病（MSD）。无论是股票搬运工和订单填料有 非工作日的工伤发生率高于平均水平。减少MSD的数量是一个 运输、仓储和公用事业（TWU）理事会和肌肉骨骼健康（MSH）的目标 跨部门诺拉计划。预防MSD依赖于有效的工作设计和工作-休息时间表， 尽量减少疲劳。然而，目前的实践依赖于为静态肌肉负荷开发的疲劳模型， 未能考虑到拣选员所经历的动态需求。因此， 建议的项目是使预测疲劳和恢复所造成的手动订单挑选，重点是 高度重复性的肩部工作。第二个目标是将研究成果转化为 通过为从业者提供这些预测模型来实践（r2 P），以便将其纳入工作中 评估和设计实践。这些目标解决了MSH跨部门议程要求研究的问题， 将实时数据与经过验证的预测模型相结合，以解决任务和工作的可变性， 休息周期。将根据实验室研究期间收集的数据构建模型。使用中心合成 设计时，将在一系列载荷水平和重复率下评估疲劳发展， 将在一定范围的休息时间内测量疲劳。受试者将完成四个阶段的订单 采摘，由指定的休息时间分开。相关测量将包括疲劳的主观评级， 来自可穿戴传感器的运动学数据和任务性能。这些措施将统一成一个疲劳 结果指标使用功能回归。然后，应用信度理论对统一结果进行预测 在分别作为退化和逆退化过程的重复疲劳和恢复循环期间， 考虑任务条件、工人特征和时间。将在合作伙伴仓库进行现场验证 执行，其中模型预测将与三个订单拣选作业的工人主观评级进行比较。 这些模型一旦得到验证，将被打包成一个基于网络的应用程序，分发给 从业者（输出），能够预测未来的工人疲劳水平，这比现有的更有信息量 提供工人当前状况或风险的快照的方法。应用修订后的模型可以 促进改善工作场所的设计和工作安排，以适应订单拣选员的能力， 支持预防肌肉骨骼疾病和改善工人健康的长期目标（结果）。

Project Summary The growing demand for e-commerce has resulted in an increase in warehouses and distribution centers, along with the needed workforce to run the operations. For improved efficiency, companies are shifting to parts-to- person systems for order fulfillment to reach productivity levels near 500 items/hour per worker. These systems create manual order picking jobs that are highly repetitive and primarily involve the arm and shoulder. Repetitive arm movements, performed for prolonged durations without adequate rest, can result in fatigue and discomfort for the shoulder, which can lead to musculoskeletal disorders (MSDs). Both stock movers and order fillers have above average incidence rates of injuries involving days away from work. Reducing the number of MSDs is an objective of the Transportation, Warehousing, and Utilities (TWU) Council and the Musculoskeletal Health (MSH) Cross-Sector NORA Agendas. Preventing MSDs depends on effective job design and work-rest schedules that minimize fatigue. However, current practice relies on fatigue models developed for static muscle loading, which fail to account for the dynamic demands experienced by order pickers. Thus, the primary objective of the proposed project is to enable prediction of fatigue and recovery resulting from manual order picking, focusing on parts-to-person systems with highly repetitious shoulder work. A secondary objective is to translate the research to practice (r2P) by providing practitioners with these predictive models to enable incorporation into their job evaluation and design practices. These objectives address the MSH cross-sector agenda call for research on the integration of real-time data with validated predictive models that address the variability in tasks and work- rest cycles. The models will be constructed from data collected during an in-lab study. Using a central composite design, fatigue development will be evaluated across a range of load levels and repetition rates, and recovery from fatigue will be measured across a range of rest durations. Subjects will complete four periods of order picking, separated by designated rest periods. Dependent measures will include subjective ratings of fatigue, kinematics data from wearable sensors, and task performance. These measures will be unified into a fatigue outcome metric using functional regression. Then, reliability theory will be applied to predict the unified outcome during repeated fatigue and recovery cycles as degradation and inverse degradation processes, respectively, accounting for task conditions, worker characteristics, and time. Field validation at a partner warehouse will be performed, where model predictions will be compared to worker subjective ratings for three order picking jobs. Once validated, the models will be packaged into a web-based application which will be disseminated to practitioners (output), enabling prediction of future worker fatigue levels, which is more informative than existing methods that provide a snapshot of the worker’s current condition or risk. Application of the revised models can facilitate improved workplace design and job scheduling to accommodate the capacities of order pickers, which supports the long-term goals of preventing musculoskeletal disorders and improving worker health (outcome).