# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 2 Related Work
### Cognitive Load: From Theory to Measurements  
[PERSON] and [PERSON] define cognitive load as \"a multidimensional construct representing the load that performing a particular task imposes on the learner's cognitive system\" [23]. As such, cognitive load is dependent on the task, the participant, and the interaction between the two. For instance, tasks may be objectively more or less demanding, people can have different cognitive capacities, and certain tasks can be easier for those who are skilled in similar tasks. This multi-dimensionality of cognitive load makes its measurement a rather challenging feat. Cognitive load measurement methods often rely on data about the subjective perception of the task difficulty, performance data using primary and secondary task techniques, and psycho-physiological data [24]. Measuring subjective data is performed using surveys (e.g., NASA-TLX [25]) solved by a user at the end of a task. However, subjective post hoc measurements are impractical in real-world applications, as they require explicit querying of users. Cognitive load measurement through a secondary task performance requires a user to attend to a simple secondary task (for instance, react to a slowly changing screen background color), while solving the primary task [26]. These techniques, too, are invasive and, in numerous situations such as while driving, not suitable for in situ cognitive load inference. Instead, physiological techniques for cognitive load measurement rely on signals, stemming from heart beat activity [27], breathing [28], heat flux [29], brain activity [30], and eye movement [31; 32]. Changes in these signals are a result of our autonomic nervous system's reaction to increased cognitive load. In [29], [PERSON] et al. used elementary cognitive tasks (ECTs), a well-established tool in educational psychology [33], to elicit different levels of cognitive engagement and monitor users' eye movement, heart and brain activity, and skin conductance while the users solved the ECTs. The authors demonstrated that two extreme levels of task difficulty (\"easy\" vs. \"difficult\") can be discriminated with 80% accuracy using heat flux, ECG features, and person-specific data, i.e., personalized models. The method, however, requires that the users are static and strapped with specialized sensors. In a study with developers engaged in real-world programming tasks, [PERSON] et al. used physiological sensors to infer human interruptibility. The study shows that EEG signals, eye blinks, skin conductance, heart rate, and inter-beat interval features correlate with interruptibility, which, in turn, negatively correlates with a user's mental load [34]. Recent advancements in sensing technology enable less intrusive forms of vital sign monitoring and get us closer towards unintrusive cognitive load inference [35]. [PERSON] et al. used commercially available Empatica wristbands and acquired signals related to heart rate variability, blood volume pulse, GSR, skin temperature, and acceleration, while exposing users to varying levels of stress [11]. The study demonstrates that off-the-shelf equipment can be used for reliable (up to 92% accuracy achieved in the study) stress detection. While a separate concept, stress may be related to cognitive load, and an earlier study by [PERSON] et al. has already shown that the same GSR sensor can be used to discriminate between the two phenomena [36]. Researchers have also attempted to unobtrusively measure cognitive load in specific environments. For instance, [PERSON] et al. used MS band to infer cognitive load in a simulated driving environment [37]. The authors argue that cheap wearables may provide enough information about physiological signals to enable binary (\"engaged in a task\" vs. \"not engaged in a task\") classification of the cognitive load, yet are unlikely suitable for inferring the actual level of cognitive load. [PERSON] et al. used the same wristbands and an N-back task to elicit different levels of cognitive load among office workers [38].