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.本数据集收录了一项实验的综合数据，该实验旨在探究工业场景装配任务中，机器人运动学变量对人机交互（Human-Robot Interaction, HRI）的影响。本研究旨在评估不同机器人运动学配置下的作业绩效、生理反应与用户感知。本次实验采用严谨设计的流程，涵盖任务前准备、任务执行与任务后复盘三个阶段，参与者使用库卡KUKA LBR iiwa 14 R820协作机器人完成装配任务。研究设置了两种差异化的机器人运行模式：慢速任务（Slow Task, ST）与快速任务（Fast Task, FT），用以模拟不同的任务工况，从而全面评估机器人运动学变量对人为因素的影响。本数据集共收录20名志愿者（10名男性、10名女性）的采集数据，受试者均匀分配至慢速-快速（Slow-Fast, SF）与快速-慢速（Fast-Slow, FS）两种实验流程中。参与者的作业绩效通过核心绩效指标进行评估，具体为任务执行时长与失误次数。生理反应通过脑电图（Electroencephalogram, EEG）与皮肤电反应/皮肤电活动（Galvanic Skin Response, GSR / Electrodermal Activity, EDA）设备采集，捕获的变量包括效价（Valence）、记忆水平（Memorisation）、心理负荷（Mental Workload）、投入度（Engagement）、激活度（Activation）与影响度（Impact）。感知指标包括实用质量（Pragmatic Quality）、享乐质量（Hedonic Quality）、可靠性（Reliability）、可控性（Controllability）与感知有用性（Perceived Usefulness），通过用户体验问卷简化版（User Experience Questionnaire-Short Form, UEQ-S）与额外自编问题进行评估。本数据集的核心组成部分如下： - 感知问卷（.pdf）：该文档包含向参与者发放的全部问卷内容。 - 原始数据文件（.xlsx）：内含四个工作表： 第一个工作表收录参与者的社会人口学信息，详细记录性别、年龄、高校任职情况、机器人使用经验与教育背景；同时包含每名参与者在慢速任务（ST）与快速任务（FT）中的任务执行数据，并按照实验流程进行整理。 第二个工作表涵盖多种t检验分析，包括任务间与流程间的对比分析。 第三个工作表对原始数据进行重组，以支持基于性别的分析；第四个工作表则展示了按性别划分的、跨任务、跨流程以及流程内任务间的额外t检验对比结果。本数据集采集自工业装配任务，详细揭示了机器人运动学变量（如速度与加速度）如何影响人类作业绩效、生理反应与用户感知。本数据集可用于优化机器人设计、开发更直观的用户界面、研究工业场景中的人为因素，以及验证人机交互仿真模型。