SenseCobotFusion

SenseCobotFusion dataset has been created as a natural evolution of SenseCobot dataset.

SenseCobotFusion dataset collects metrics extracted from ElectroCardioGram (ECG), Galvanic Skin Response (GSR), ElectroEncephaloGram (EEG), and emotion signals obtained with professional biosensors, according to modern state-of-the-art signal processing methods, labeled with a subjective evaluation obtained from widely used NASA-TLX questionnaire.

The signals used for this processing 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, organized in tasks of increasing complexity: Task 1, Task 2, Task 3, Task 4 and Task 5. 

SenseCobotFusion is organized to facilitate statistical investigations, data mining, and machine learning applications as much as possible, and divided into participants and tasks performed: a practical Readme.txt file contains details of the metrics extracted, the nature of the signals of origin, and information on the use of the dataset itself.

A Python code present in this repository has been implemented and optimized with modern state-of-the-art libraries and algorithms to support the researcher in analyzing SenseCobot data (https://zenodo.org/records/10124005), similar datasets, or new related biological signals.

Classic machine learning models such as Decision Tree, Random Forest, SVM, and XGBoost have been trained on SenseCobotFusion to showcase the dataset potential and uploaded to this repository in pickle file format.

Integration with its predecessor SenseCobot dataset, allows the user to implement various types of analysis, such as time series and deep learning approaches.

The SenseCobotFusion dataset, building on the SenseCobot dataset, supports HRC research by providing high-quality, multimodal metrics on mental effort and stress during cobot programming, offering valuable insights for developing intuitive programming interfaces, predictive machine-learning models for real-time stress monitoring, and enhancing human-robot collaboration, while also enabling integration with other datasets, statistical investigation of physical and mental states in Industry 5.0, user-specific machine learning model customization, and the creation of adaptive platforms or technologies aligned with the SenseCobotFusion protocol.If the SenseCobotFusion_Code code or the SenseCobotFusion dataset is used in whole or in part please credit the authors and this repository.