Impact of Robotic kinematic variables on User Experience: Dataset on Performance, Physiological Response, and User Perception in Human-Robot Interaction during an Assembly Task

This dataset presents comprehensive data derived from an experiment aimed at investigating the influence of robot kinematic variables on human-robot interaction (HRI) during assembly tasks in an industrial setting. The study sought to evaluate performance, physiological responses, and user perceptions associated with different robot kinematic configurations.
Through a meticulously designed experimental procedure comprising pre-task execution, task execution, and post-task execution phases, participants engaged in an assembly task using a KUKA LBR iiwa 14 R820 collaborative robot. Two distinct robot behaviors, Slow Task (ST) and Fast Task (FT), were programmed to simulate different task conditions, allowing for a comprehensive assessment of the impact of robot kinematic variables on human factors.
The dataset includes data collected from 20 volunteers (10 men and 10 women) evenly distributed across two procedures: Slow-Fast (SF) and Fast-Slow (FS). Participants' performance was evaluated based on key performance indicators, concretely, task execution time and errors. Physiological responses were measured using EEG and GSR/EDA devices, capturing variables such as Valence, Memorisation, Mental Workload, Engagement, Activation, and Impact. Perceptual indicators, including Pragmatic Quality, Hedonic Quality, Reliability, Controllability, and Perceived Usefulness, were assessed through UEQ-S and and additional self-generated questions. Key components of the dataset include:
- Perceptual questionnaire (.pdf): This document contains the questionnaire provided to participants.
- The Raw data (.xlsx) file consists of four tabs: The first tab contains sociodemographic information of participants, detailing gender, age, university role, robot experience, and educational background. It also presents task execution data collected during both the Slow Task (ST) and Fast Task (FT) for each participant, organized according to the experimental procedure. The second tab encompasses various T-test analyses, including comparisons between tasks, and procedures. The third tab reorganized the raw data for gender-based analysis, and the fourth tab shows additional T-test comparisons by gender across tasks, procedures, tasks within procedures.
The collected data from industrial assembly tasks provides detailed perspectives on how robot kinematic variables, such as speed and acceleration, impact human performance, physiological responses, and user perceptions. This dataset can be utilized to optimize robot design, develop more intuitive user interfaces, study human factors in industrial settings, and validate human-robot interaction simulation models.