As industries progress toward integrating more complex technologies within Industry 4.0 frameworks, ensuring work instructions that balance cognitive load and performance is increasingly critical, especially under the human-centric principles of the 5th industrial revolution. Drawing on Cognitive Load Theory (CLT), this study compares two instructional methods-visual-based and code-based-to determine whether cognitive overload can be reduced without compromising task outcomes in a controlled, assembly-like scenario derived from industrial tasks. We recruited 30 participants from the academic field (students and researchers), who completed assembly tasks under both visual-based and code-based instructions. Cognitive load was measured objectively by (Galvanic Skin Response, Heart Rate Variability, and hand motion acceleration) and subjectively through (NASA Task Load Index, short Dundee Stress State Questionnaire). Operational efficiency was assessed via task completion time (TCT), number of task repetitions (NTR), and assembly precision based on the standard deviation. The findings demonstrated that visual-based instructions significantly reduced cognitive load with a p - value < 0.001. It also showed an improvement in two of the performance metrics during the use of visual-based instructions for the TCT and NTR with p - value < 0.001. However, although code-based instructions increased cognitive load, they showed better assembly precision with a p - value < 0.001. These results suggest that while simple and direct instructions facilitate task execution and reduce cognitive loads, deep thinking approaches may still hold value for tasks requiring high precision.
Ethical approval number: KEB_MK_FIT_2024_01
Human-robot collaboration promises to free the human to multitask and engage in cognitive work while the robots assists with physical tasks, therefore increasing productivity. However, this collaborative paradigm requires continuous attention from human operators, which could potentially strain their cognitive resources. Excessive attention demands can lead to safety hazards, increased errors, and reduced efficiency. Despite its critical importance, there is limited empirical research on attentional factors in industrial human-robot collaboration. In this study, we explore attentional multitasking in collaborative human-robot assembly settings. Our experimental setup involves participants performing a wire harnessing task with a collaborative robot while simultaneously completing a Go/No-Go test as a secondary task. To observe the effect of multitasking, we varied the difficulty of the secondary task across two levels and analysed its impacts on work performance and workload. Our results confirm threaded cognition theory, suggesting that human-robot collaboration could reduce cognitive capacity by depleting attentional resources, leading to higher errors and cycle times during multitasking. This underscores the importance of a detailed understanding of attentional factors in human-robot collaboration. We discuss our findings and their implications, and provide insights into the adjustment and design of human-robot collaboration tasks in the industry.
This dataset was collected from an exploratory dual-task experiment conducted to investigate attentional demands and physiological correlates of situational awareness in industrial-like multitasking scenarios. The experiment involved 12 participants performing a manual screwing task while simultaneously responding to a Go/No-Go attentional test designed to emulate real-world industrial multitasking demands. Physiological data including electrocardiogram (ECG) and electrodermal activity (EDA), alongside hand acceleration data, were recorded using wearable sensors. Reaction times to Go/No-Go stimuli were also collected as indicators of attentional load. The dataset consists of event-based features extracted from these signals, providing insights into the relationship between physiological arousal and task performance. For detailed methodology and analysis, please refer to our publication: "Studying Dual-Task Awareness in Industrial Settings Through Reaction Time and Physiological Signals". Kindly cite this paper when utilizing this dataset in your research.
Ethical approval number: RK-3/4/2025
Considering the work characteristics of the baristas, the work content can be categorized into mental workload as the difficulty of the incoming ordered drinks, physical workload as the frequency and intensity of the physical activities during making and serving drinks, and temporal workload as the required work pace. The work context that also poses stress on the barista includes the interactions with customers, and non-work-content-related activities that are not associated with the number of ordered drinks (e.g., floor cleaning, interaction with the managers). The work environment contains factors that reflect the physical environment, such as working area, ergonomic arrangement, lighting condition, noise, and temperature, etc.
The coffee shop’s confined environment and compact layout limited the number of employees to only one barista working at a time, naturally isolating work content as the primary factor influencing stress and performance, similar to a controlled environment in laboratory setup.
Ethical approval number: KEB_MK_RIT_2023_01
This research explores how verbal distractions influence the learning process during an assembly task. Participants first practiced a pattern replication task until their performance stabilized, then repeated it under conversation-based distractions. A camera system with ArUco marker tracking and timer-based monitoring was used to assess both accuracy and task completion time. The results showed that while distractions significantly increased task duration, they did not negatively affect the quality of the final product. These findings underline the importance of considering cognitive load in industrial environments and demonstrate the effectiveness of video-based monitoring for evaluating human performance.
Ethical approval number: KEB_MK_RIT_2023_02
Due to high turnover in industry, efficient onboarding and supportive systems are essential. This research investigates how operators learn a new task and how they interact with different types of work instructions. In a disassembly experiment, we measured task completion time, error rates, and attention to the instructions. Our findings show that simplified work instructions introduced after an initial training phase can significantly reduce task duration without increasing mistakes. These results highlight the potential of instruction abstraction in supporting operator performance during repetitive tasks.
Ethical approval number: 2025-028