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

仓库操作员执行动态任务的肩部疲劳和恢复的可靠性建模

• 批准号: 9896072
• 负责人: Lora Anne Cavuoto
• 金额: $ 18.84万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9896072
• 关键词: Reliability; Modeling; Shoulder; Fatigue; Recovery

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).

项目摘要 对电子商务的需求不断增长，导致仓库和分销中心的增加 使用所需的劳动力来运行操作。为了提高效率，公司正在转移到零件 - 订单履行的人员系统，每位工人每小时达到500件物品，达到生产率水平。这些系统 创建高度重复的手动订单挑选工作，主要涉及手臂和肩膀。重复 手臂运动，长时间进行持续时间，没有足够的休息，可能会导致疲劳和不适 对于肩膀，可能导致肌肉骨骼疾病（MSD）。两种库存的动作和订单填充剂都有 高于平均伤害参与日的发病率远离工作。减少MSD的数量是 运输，仓库和公用事业（TWU）理事会和肌肉骨骼健康（MSH）的目标 跨部门诺拉议程。防止MSDS取决于有效的工作设计和工作时间表 最小化疲劳。但是，当前的实践依赖于用于静态肌肉负荷开发的疲劳模型，这 无法说明订单选择器所经历的动态需求。那，是 拟议的项目是为了预测手动订单的疲劳和恢复，重点是 零件系统具有高度重复的肩部工作。次要目标是翻译研究 通过为实践者提供这些预测模型来实践（R2P） 评估和设计实践。这些目标涉及MSH跨部门议程呼吁进行研究 实时数据与经过验证的预测模型的集成，以解决任务和工作的可变性 - 休息周期。这些模型将由在LAB研究期间收集的数据构建。使用中央复合材料 设计，疲劳开发将在一系列负载水平和重复率和恢复范围内进行评估 疲劳将在一系列休息时间内测量。受试者将完成四个订单 采摘，由指定的休息时间隔开。依赖措施将包括疲劳的主观评分， 可穿戴传感器和任务性能的运动学数据。这些措施将统一为疲劳 使用功能回归的结果指标。然后，将应用可靠性理论来预测统一结果 在反复疲劳和恢复周期中，分别作为降解和逆降解过程 考虑任务条件，工人特征和时间。合作伙伴仓库的现场验证将是 执行，将模型预测与三个订单挑选工作的工人主观评分进行比较。 一旦验证，这些模型将被包装到基于Web的应用程序中 从业者（输出），可以预测未来的工人疲劳水平，这比现有 提供工人当前状况或风险的快照的方法。修订模型的应用可以 促进改进的工作场所设计和工作时间表，以适应订单挑选者的能力， 支持防止肌肉骨骼疾病和改善工人健康（结果）的长期目标。