Learning performance and physiological feedback-based evaluation for human–robot collaboration

Abstract

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.