Lin Chiuhsiang Joe, Lukodono Rio Prasetyo
Industrial Management, National Taiwan University of Science and Technology, Taiwan.
Industrial Engineering, Universitas Brawijaya, Indonesia.
Appl Ergon. 2025 Apr;124:104425. doi: 10.1016/j.apergo.2024.104425. Epub 2024 Nov 28.
The development of Industry 4.0 has resulted in tremendous transformations in the manufacturing sector to supplement the human workforce through collaboration with robots. This emphasis on a human-centered approach is a vital aspect in promoting resilience within manufacturing operations. In response, humans need to adjust to new working conditions, including sharing areas with no apparent separations and with simultaneous actions that might affect performance. At the same time, wearable technologies and applications with the potential to gather detailed and accurate human physiological data are growing rapidly. These data lead to a better understanding of evaluating human performance while considering multiple factors in human-robot collaboration. This study uses an approach for assessing human performance in human-robot collaboration. The assessment scenario necessitates understanding of how humans perceive collaborative work based on several indicators, such as perceptions of workload, performance, and physiological feedback. The participants were evaluated for around 120 min. The results showed that human performance improved as the number of repetitions increased, and the learning performance value was 92%. Other physiological indicators also exhibited decreasing values as the human performance tended to increase. The findings can help the industry to evaluate human performance based on workload, performance, and physiological feedback information. The implication of this assessment can serve as a foundation for enhancing resilience by refining work systems that are adaptable to humans without compromising performance.
工业4.0的发展给制造业带来了巨大变革，通过与机器人协作来补充人力。这种以人为本的方法是提升制造运营韧性的关键方面。作为回应，人类需要适应新的工作条件，包括共享无明显分隔的区域以及可能影响绩效的同步行动。与此同时，能够收集详细准确的人类生理数据的可穿戴技术和应用正在迅速发展。这些数据有助于在考虑人机协作中的多个因素时更好地理解和评估人类绩效。本研究采用一种评估人机协作中人类绩效的方法。评估场景需要了解人类如何基于几个指标来感知协作工作，如工作量感知、绩效感知和生理反馈。参与者接受了约120分钟的评估。结果表明，随着重复次数的增加，人类绩效有所提高，学习绩效值为92%。随着人类绩效趋于提高，其他生理指标也呈现出下降趋势。这些发现有助于该行业基于工作量、绩效和生理反馈信息来评估人类绩效。这种评估的意义可以作为通过优化适应人类且不影响绩效的工作系统来增强韧性的基础。
