SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0

The cornerstone of Industry 5.0 is the human, its well-being, development, and creativity at the center of the production process. To achieve this, recording emotional, psychological, physical, and cognitive states efficiently in real-time is crucial. In particular, monitoring a complex psychophysiological state such as stress requires obtaining information from reliable biological signals such as electrocardiogram (ECG), galvanic skin response (GSR), electroencephalogram (EEG), and facial expressions. In this study, we introduce a new dataset, SenseCobotFusion, which collects stress-related metrics derived from physiological signals recorded from operators engaged in Human-Robot Collaboration (HRC) tasks. Labeled with the subjective operator rating obtained with the NASA-TLX questionnaire tool, SenseCobotFusion is a new dataset available to the research community focused on stress and workload detection. SenseCobotFusion is structured to be flexible and compatible with other existing datasets and potential experimental scenarios. To achieve this, a thorough dataset encompassing all metrics and specific sub-datasets for each signal type was developed, enabling seamless adaptation to user needs. As a tentative example of the potential of SenseCobotFusion, machine learning models were trained on each of these datasets. The results align with findings in the literature: the stress response is highly subjective and influenced by numerous factors, both dependent and independent of the operator. Additionally, the signal processing pipeline codes used to extract the metrics of interest, specific for GSR, EEG, ECG, and Emotions data, was also provided, which can be used as a guideline to extract stress-related metrics from SenseCobot and similar datasets.