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Human-Robot Collaboration (HRC), where humans and robots work together, is revolutionizing construction site operations. While HRC offers significant productivity gains by reducing the physical demands on workers, it also presents a new challenge: cognitive overload. This study explores Electrodermal Activity (EDA) as a measurable indicator of the cognitive load experienced by workers during their interaction with robots. Utilizing a robotic arm and wearable wristbands for experiments, EDA signals were recorded from two groups: the HRC assembly group and the solely assembly group. The results revealed a noticeable difference in EDA levels between the participants in the HRC setup compared to those working independently. Specifically, the Skin Conductance (SC) feature, normalized with baseline data, outperformed other EDA signals such as SCL, PhasicMax, ISCR, and SCR, achieving an F-score of 79%. By contributing to the broader discussion on adopting HRC in the construction industry, this research highlights the importance of developing strategies to alleviate cognitive load on workers, thereby maximizing the benefits of HRC.

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Electrodermal Activity as an Indicator of Cognitive Load for Human-Robot Collaboration in Construction

  • 会议论文

  • 发表日期：

    2025年

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  • Seulbi Lee,
  • Bogyeong Lee

摘要

Human-Robot Collaboration (HRC), where humans and robots work together, is revolutionizing construction site operations. While HRC offers significant productivity gains by reducing the physical demands on workers, it also presents a new challenge: cognitive overload. This study explores Electrodermal Activity (EDA) as a measurable indicator of the cognitive load experienced by workers during their interaction with robots. Utilizing a robotic arm and wearable wristbands for experiments, EDA signals were recorded from two groups: the HRC assembly group and the solely assembly group. The results revealed a noticeable difference in EDA levels between the participants in the HRC setup compared to those working independently. Specifically, the Skin Conductance (SC) feature, normalized with baseline data, outperformed other EDA signals such as SCL, PhasicMax, ISCR, and SCR, achieving an F-score of 79%. By contributing to the broader discussion on adopting HRC in the construction industry, this research highlights the importance of developing strategies to alleviate cognitive load on workers, thereby maximizing the benefits of HRC.