SenseCobot

SenseCobot dataset has been created to evaluate stress level and cognitive load in participants involved in cobot programming tasks. This dataset integrates various physiological signals, including ElectroCardioGram (ECG), Galvanic Skin Response (GSR), body temperature, ElectroDermal Activity (EDA), ElectroEncephaloGram (EEG), Blood Volume Pulse (BVP), facial emotions, and subjective responses from NASA-TLX questionnaires.
These signals have been obtained from 21 participants engaged in collaborative robotics programming tasks, organized in three phases: an introduction to learning materials, a baseline measurement task to establish reference conditions, and hands-on practice. Different wearable and non-invasive sensors have been used, such as Shimmer 3 ECG, EEG Enobio 20 channels helmet, Shimmer 3 GSR, Empatica E4 and wristband sensors. The dataset is organized into folders based on the type of collected signals, making it user-friendly and accessible for research purposes. Txt format files have been added in each folder containing detailed information about individual signals.
SenseCobot dataset, aims to support research in HRC by providing high-quality, multimodal physiological data related to mental effort and stress during cobot programming. Such data can be valuable for developing more intuitive and user-friendly programming interfaces, predictive machine learning models for real-time stress monitoring, and enhancing human-robot collaboration in various applications.