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This study introduces two datasets for multimodal research on cognitive load inference and 1 personality traits. Different to other datasets in Affective Computing, which disregard participants’ 2 personality traits, or focus only on emotions, stress, or cognitive load from one specific task, the 3 participants in our experiments performed seven different tasks in total. In the first dataset, 23 4 participants played a varying difficulty (easy, medium and hard) game on a smartphone. In the second 5 dataset, 23 participants performed six psychological tasks on a PC, again with a varying difficulty. In 6 both experiments, the participants filled personality-traits questionnaire and marked their perceived 7 cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response 8 was recorded using a wrist-device measuring heart rate, beat-to-beat intervals, galvanic skin response, 9 skin temperature, and 3-axis acceleration. The datasets allow multimodal study of physiological 10 responses of individuals in relation to their personality and cognitive load. Various analyses of 11 relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective 12 cognitive load (i.e., task difficult) are presented. Additionally, baseline machine learning models 13 for recognizing task difficulty are presented, including a multi task learning (MTL) neural network 14 that outperforms single task neural network by simultaneously learning from the two datasets. The 15 datasets are publicly available to advance the field of cognitive load inference using commercially 16 available devices. 17

Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits