https://hdl.handle.net/20.500.11984/1234 Fri, 26 Jun 2026 03:26:43 GMT 2026-06-26T03:26:43Z https://hdl.handle.net/20.500.11984/14500 Experts’ Evaluation: Datasets of Rating Questionnaires for the HEUROBOX Heuristics to Evaluate Industrial Collaborative Robotic Environments Apraiz, Ainhoa; Mulet Alberola, Jose Antonio; Lasa, Ganix; Mazmela Etxabe, Maitane; Nguyen Thi Ngoc, Hien This study presents a dataset derived from expert evaluation aimed at assessing two aspects: (1) the usability of a newly developed set of heuristics (HEUROBOX), and (2) the relative importance of heuristic categories in evaluating collaborative robotic environments for industrial applications. The experts were carefully selected based on their expertise in various relevant fields, including collaborative robotics, human factors, biomechanics, and manufacturing. Their evaluations were obtained through the System Usability Scale (SUS) questionnaire and category-rating questionnaires, which were employed in conjunction with the Analytical Hierarchy Process (AHP) methodology. The resulting dataset was analyzed using AHP algorithms implemented in the R programming language. The transparency of both the data and the code allows design practitioners and researchers to utilize them for replication and further analysis, thereby adhering to the principles of reproducible research in the context of multiple-criteria decision analysis. Sun, 01 Jan 2023 00:00:00 GMT https://hdl.handle.net/20.500.11984/14500 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14498 Impact of Robotic kinematic variables on User Experience: Dataset on Performance, Physiological Response, and User Perception in Human-Robot Interaction during an Assembly Task Apraiz, Ainhoa; Lasa, Ganix; Mazmela Etxabe, Maitane; Arana-Arexolaleiba, Nestor; Elguea, Íñigo; Etxabe, Amaia; Serrano, Antonio 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. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.11984/14498 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14497 ACTi Graphs: Data Collections from Physiological Experiments on Stress and Energy Expenditure Apraiz, Ainhoa; Blandino, Graziana; Tarhini, Ali; Pratas, Pedro; Al Noman, Abdullah; Nguyen Ngoc, Hien; Lasa, Ganix; Montagna, Francesca; Escallada Lopez, Oscar; da Luz Covas, Maria; Guedes, Joana; Guerra, Ana; Ramião, Nilza; Thomann, Guillaume; Bustos, Denisse This database brings together data collected from ActiGraphs worn by test participants. Each participant wore three ActiGraphs placed on the ankle, waist, and wrist during the tests. The published data has been pre-processed using ActiLife software. This database enabled comparisons between energy expenditure, the number of mistakes made, and participants' self-reported stress levels. The data is organised into folders, each corresponding to a specific participant. Within each participant’s folder, there are three subfolders and a file. The subfolders contain pre-processed data from the ActiGraphs, categorised by the body location where each sensor was worn (ankle, waist, and wrist). The file named "performance.xlsx" provides details on the participant's performance during the test. The pre-processed ActiGraph files display the counts from the sensor axes. These counts are calculated by ActiLife software, using a proprietary method that the company has not disclosed. Additional information, such as inclinometer readings and step counts, is also included in these files, although this data was not used in the study. The "performance.xlsx" file outlines the conditions and results of each task performed by the participants. It specifies the start and end times of each task, the number of errors made, and details about environmental and noise conditions ("Temperature" and "Noise"). This file also contains participants’ responses to a stress questionnaire administered at the end of each task. The questionnaire is available in the attached document titled "Stress Questionnaire.pdf". Additionally, the "NoStress_Processing.py" Python script is provided for data processing. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.11984/14497 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14418 Cobertura retos por framework Cayón, Izaskun; Izaola, Zunbeltz; de la Peña Sordo, Jorge This repository contains data and additional materials of the paper "Framework Benchmarking for Industrial Data Governance: A Coverage-Oriented Assessment" by Izaskun Cayón-Camarero (Mondragon University, Spain & Mondragon Innovation and Knowledge, Spain), Zunbeltz Izaola (Mondragon University, Spain & Mondragon Innovation and Knowledge, Spain) and Jorde de-la-Peña-Sordo (Mondragon University, Spain & Mondragon Innovation and Knowledge, Spain). This file contains the empirical evidence collected during the study, as well as the evaluation rubric used for data analysis. Its purpose is to ensure the transparency, traceability, and reproducibility of the results presented in the main paper. Thu, 01 Jan 2026 00:00:00 GMT https://hdl.handle.net/20.500.11984/14418 2026-01-01T00:00:00Z