# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
###### Abstract  
This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants' personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants' physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices. cognitive load; dataset; Affective Computing; machine learning; physiology; personality traits; sensor data +
Footnote †: journal: Medical  
are directly related to psychological states, e.g., mobile healthcare [2]. One of the main impediment factors for the advance of Affective Computing research is its reliance on real-world user studies that often need to involve elaborate experimental protocols and physiological signal measurements. Being difficult to gather, datasets that include various aspects of human internal states (e.g., the participants' impressions, personality traits, etc.) as well as detailed physiological signal measurements during the experiments are rare and seldom publicly available. An important aspect of Affective Computing is the cognitive load inference. There are different reasons why ubiquitous computing devices would benefit from being aware of their users' cognitive load, the most important of which is likely to prevent the undesirable effects of attention grabbing at times when a user is occupied with a difficult task. Research has repeatedly shown that improperly timed notifications can be distracting, causing a negative effect on task performance [3; 4; 5; 6; 7; 8], increasing stress [9], and reducing well-being [10]. Fortunately, increased cognitive load is reflected in a measurable signal change. When humans experience a psycho-physiological load, e.g., in the form of a demanding task, activation of the sympathetic nervous system increases [11]. This increased activation translates into changes in the blood pressure [12], heart rate variability [13], respiration [14], brain activity [15], galvanic skin response (GSR) [16; 17], eye movement [15], pupil size, facial expressions [18], and other factors. The physiological changes can be measured with special equipment, e.g., a nasal thermistor, a chest respiration, electrocardiogram(ECG), a sphygmomanometer (blood pressure monitor), and electroencephalography (EEG), to name a few. Yet, the high cost, bulkiness, and the fact that they work only if a user is static and strapped with sensors all limit the applicability of these devices in ubiquitous computing. In this paper, we focus on two recent data collection campaigns [19; 20] that capture the above reactions using off-the-shelf equipment, thus greatly expanding the potential applications in which the knowledge of a user's cognitive load can be harnessed.