Multi-Modal Measurements of Mental Load (original) (raw)
Related papers
Integrated real-time, non-intrusive Measurements for Mental Load
2019
In this position paper, we propose to develop a system that takes input data from different sensors on physiological behaviors such as Pupil Diameter, Blinking Rate, Heart Rate, Heart Rate Variability, and Galvanic Skin Response to estimate users’ mental load (see Sidebar 1). We firstly aim to collect data on these behaviours and then intend to understand the correlation between them and later want to predict an estimate of cognitive load in real-time. We hope to use this measure during Human-Computer Interaction and Human-Robot Interaction to personalize our interfaces or robots’ behaviour according to user mental load in the real-time.
Modeling cognitive load and physiological arousal through pupil diameter and heart rate
Multimedia Tools and Applications, 2018
This study investigates individuals' cognitive load processing abilities while engaged on a decision-making task in serious games, to explore how a substantial cognitive load dominates over the physiological arousal effect on pupil diameter. A serious game was presented to the participants, which displayed the on-line biofeedback based on physiological measurements of arousal. In such dynamic decision-making environment, the pupil diameter was analyzed in relation to the heart rate, to evaluate if the former could be a useful measure of cognitive abilities of individuals. As pupil might reflect both cognitive activity and physiological arousal, the pupillary response will show an arousal effect only when the cognitive demands of the situation are minimal. Evidence shows that in a situation where a substantial level of cognitive activity is required, only that activity will be observable on the pupil diameter, dominating over the physiological arousal effect indicated by the pupillary response. It is suggested that it might be possible to design serious games tailored to the cognitive abilities of an individual player, using the proposed physiological measurements to observe the moment when such dominance occurs.
Psycho-physiological measures for assessing cognitive load
Proceedings of the 12th ACM international conference on Ubiquitous computing - Ubicomp '10, 2010
With a focus on presenting information at the right time, the ubicomp community can benefit greatly from learning the most salient human measures of cognitive load. Cognitive load can be used as a metric to determine when or whether to interrupt a user. In this paper, we collected data from multiple sensors and compared their ability to assess cognitive load. Our focus is on visual perception and cognitive speed-focused tasks that leverage cognitive abilities common in ubicomp applications. We found that across all participants, the electrocardiogram median absolute deviation and median heat flux measurements were the most accurate at distinguishing between low and high levels of cognitive load, providing a classification accuracy of over 80% when used together. Our contribution is a real-time, objective, and generalizable method for assessing cognitive load in cognitive tasks commonly found in ubicomp systems and situations of divided attention.
Towards measuring cognitive load through multimodal physiological data
Cognition, Technology & Work, 2020
Cognitive load plays an important role during learning and working, as it has been linked to wellfunctioning cognitive processes, performance, burnout and depression. Nonetheless, attempts to assess cognitive load in real-time by means of physiological data have been proven difficult, and interpreting these data remains challenging. The aim of this study is to examine whether and how well experienced cognitive load can be measured through psychophysiological data. The approach of this study is rather unique, for a combination of reasons. First, this study takes a multimodal approach, monitoring EDA (electrodermal activity), EEG (electroencephalography) and EOG (electrooculography). Second, this study is based on a relatively intensive data collection (N = 46) in a controlled lab setting in which varying cognitive load levels are deliberately induced. Finally, not only focussing on statistical significance, but also on the size of the association gives insights into how suitable physiological markers are to measure cognitive load. Results from a multilevel analysis suggest that the following physiological markers might be related to cognitive load, for example, in an industrial context: the rate and the duration of skin conductance responses, the alpha power, the alpha peak frequency and the eye blink rate. About 22.8% of the variance in self-reported cognitive load can be explained using these five measures.
Task Load Estimation and Mediation Using Psycho-physiological Measures
Proceedings of the 21st International Conference on Intelligent User Interfaces, 2016
Human performance falls off predictably with excessive task difficulty. This paper reports on a search for a task load estimation metric. Of the five physiological signals analyzed from a multitasking study, only pupil dilation measures correlated well with real-time task load. The paper introduces a novel task load estimation model based on pupil dilation measures. We demonstrate its effectiveness in a multitasking driving scenario. Autonomous mediation of notifications using this model significantly improved user task performance compared to no mediation. The model showed promise even when used outside in a car. Results were achieved using low-cost cameras and open-source measurement tools lending to its potential to be used broadly.
International Journal of Psychophysiology, 2013
Monitoring mental load for optimal performance has become increasingly central with the recently evolving need to cope with exponentially increasing amounts of data. This paper describes a non-intrusive, objective method to estimate mental workload in an immersive virtual reality system, through analysis of frequencies of pupil fluctuations. We tested changes in mental workload with a number of task-repetitions, level of predictability of the task and the effect of prior experience in predictable task performance, on mental workload of unpredictable task performance. Two measures were used to calculate mental workload: the ratio of Low Frequency to High Frequency components of pupil fluctuations, and the High Frequency alone, all extracted from the Power Spectrum Density of pupil fluctuations. Results show that mental workload decreases with a number of repetitions, creating a mode in which the brain acts as an automatic controller. Automaticity during training occurs only after a minimal number of repetitions, which once achieved, resulted in further improvements in the performance of unpredictable motor tasks, following training in a predictable task. These results indicate that automaticity is a central component in the transfer of skills from highly predictable to low predictable motor tasks. Our results suggest a potentially applicable method to brain-computer-interface systems that adapt to human mental workload, and provide intelligent automated support for enhanced performance.
A framework to estimate cognitive load using physiological data
Personal and Ubiquitous Computing, 2020
Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diam...
2014
We are increasingly in situations of divided attention, subject to interruptions, and having to deal with an abundance of information. Our cognitive load changes in these situations of divided attention, task interruption or multitasking; this is particularly true for older adults. To help mediate our finite attention resources in performing cognitive tasks, we have to be able to measure the real-time changes in the cognitive load of individuals. This paper investigates how to assess real-time cognitive load based on psycho-physiological measurements. We use two different cognitive tasks that test perceptual speed and visio-spatial cognitive processing capabilities, and build accurate models that differentiate an individual's cognitive load (low and high) for both young and older adults. Our models perform well in assessing load every second with two different time windows: 10 seconds and 60 seconds, although less accurately for older participants. Our results show that it is possible to build a realtime assessment method for cognitive load. Based on these results, we discuss how to integrate such models into deployable systems that mediate attention effectively.
Towards Cognitive Load Assessment Using Electrooculography Measures
2023
Cognitive load assessment is crucial for understanding human performance in various domains. This study investigates the impact of different task conditions and time constraints on cognitive load using multiple measures, including subjective evaluations, performance metrics, and physiological eye-tracking data. Fifteen participants completed a series of primary and secondary tasks with different time limits. The NASA-TLX questionnaire, reaction time, inverse efficiency score, and eye-related features (blink, saccade, and fixation frequency) were utilized to assess cognitive load. The study results show significant differences in the level of cognitive load required for different tasks and when under time constraints. The study also found that there was a positive correlation (r = 0.331, p = 0.014) between how often participants blinked their eyes and the level of cognitive load required but a negative correlation (r = -0.290, p = 0.032) between how often participants made quick eye movements (saccades) and the level of cognitive load required. Additionally, the analysis revealed a significant negative correlation (r = -0.347, p = 0.009) and (r = -0.370, p = 0.005) between fixation and saccade frequencies under time constraints.