Lamooki Saeb Ragani, Hajifar Sahand, Kang Jiyeon, Sun Hongyue, Megahed Fadel M, Cavuoto Lora A
Department of Mechanical Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
Appl Ergon. 2022 Jul;102:103732. doi: 10.1016/j.apergo.2022.103732. Epub 2022 Mar 12.
Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.
现有的人体工程学风险评估工具需要监测多个风险因素。为了消除直接观察，我们研究了一个端到端框架的有效性，该框架使用来自单个可穿戴传感器的数据。该框架用于将执行的任务识别为主要的情境风险因素，然后将任务持续时间和重复次数估计为任务强度的两个主要指标。为了评估该框架，我们招募了37名参与者，在实验室环境中完成10项模拟工作任务。在测试中，我们在任务识别方面的平均准确率达到了92%，任务持续时间估计的误差为7.3%，任务重复次数计数的误差为7.1%。此外，我们展示了该框架输出在两种人体工程学工具中的效用，以估计受伤风险。总体而言，我们表明了使用可穿戴传感器数据在工作场所实现人体工程学风险评估自动化的可行性。
