Human Cognitive Workload Research Articles

Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals

Background and ObjectiveEvaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to discrete CW levels with deep learning tools. However, the EEG distribution varies when humans perform different cognitive tasks. There is thus a question on the capacity for generalizing the CW recognizer across tasks. In this study, we examined the CW's performance when it was trained and tested on two EEG databases corresponding to different human-machine tasks. MethodsA novel deep neural network-based EEG recognizer, dynamic residual network with attention mechanism (DRNA-Net), is proposed in the present study. By taking advantage of recurrent networks, the DRNA-Net further incorporates a self-attention mechanism in discovering robust EEG patterns across different cognitive tasks. ResultsWe designed an experiment that applied a multidimensional N-back task to induce the CW that consists of visual and auditory memory tasks. We validated the cross-task generalization capability of the DRNA-Net based on the EEG features extracted from the N-back task and a public database. The results show that the DRNA-Net achieves classification accuracy and Macro-F1 values are 0.6055 and 0.6067, respectively. ConclusionsThe performance of the DRNA-Net indicates that it has a certain ability of cross-task cognitive workload classification, which outperforms several shallow learners and deep convolutional neural networks under various conditions of the feature subsets.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability.

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation

Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life. The aim of this study was to identify areas across the PV value chain that can be augmented by cognitive service solutions using the methodologies of contextual analysis and cognitive load theory. It will also provide a framework of how to validate these PV cognitive services leveraging the acceptable quality limit approach. The data used to train the cognitive service were an annotated corpus consisting of 20,000 ICSRS from which we developed a framework to identify and validate 40 cognitive services ranging from information extraction to complex decision making. This framework addresses the following shortcomings: (1) needing subject-matter expertise (SME) to match the artificial intelligence (AI) model predictions to the gold standard, commonly referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, (3) automated validation of prediction missing context, and (4) auto-labeling causing inaccurate test accuracy. The method consists of (1) conducting contextual analysis, (2) assessing human cognitiveworkload,(3) determining decision points for applying artificial intelligence (AI), (4) defining the scope of the data, or annotated corpus required for training and validation of the cognitive services, (5) identifying and standardizing PV knowledge elements, (6) developing cognitive services, and (7) reviewing and validating cognitive services. By applying the framework, we (1) identified 51 decision points as candidates for AI use, (2) standardized the process to make PV knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and context, (4) standardized acceptability by using established quality inspection principles, and (5) validated a total of 126 cognitive services. The value of using AI methodologies in PV is compelling; however, as PV is highly regulated, acceptability will require assurances of quality, consistency, and standardization. We are proposing a foundational framework that the industry can use to identify and validate services to better support the gathering of quality data and to better serve the PV professional.

Multiple-Robot Systems for USAR: Key Design Attributes and Deployment Issues

The interaction between humans and robots is undergoing an evolution. Progress in this evolution means that humans are close to robustly deploying multiple robots. Urban search and rescue (USAR) can benefit greatly from such capability. The review shows that with state of the art artificial intelligence, robots can work autonomously but still require human supervision. It also shows that multiple robot deployment (MRD) is more economical, shortens mission durations, adds reliability as well as addresses missions impossible with one robot and payload constraints. By combining robot autonomy and human supervision, the benefits of MRD can be applied to USAR while at the same time minimizing human exposure to danger. This is achieved with a single-human multiple-robot system (SHMRS). However, designers of the SHMRS must consider key attributes such as the size, composition and organizational structure of the robot collective. Variations in these attributes also induce fluctuations in issues within SHMRS deployment such as robot communication and computational load as well as human cognitive workload and situation awareness (SA). Research is essential to determine how the attributes can be manipulated to mitigate these issues while meeting the requirements of the USAR mission.

An eye movement study for identification of suitable font characters for presentation on a computer screen.

We are experiencing a shifting of media: from the printed paper to the computer screen. This transition is modifying the process of how we read and understand a text. It is very difficult to conclude on suitability of font characters based upon subjective evaluation method only. Present study evaluates the effect of font type on human cognitive workload during perception of individual alphabets on a computer screen. Twenty six young subjects volunteered for this study. Here, subjects have been shown individual characters of different font types and their eye movements have been recorded. A binocular eye movement recorder was used for eye movement recording. The results showed that different eye movement parameters such as pupil diameter, number of fixations, fixation duration were less for font type Verdana. The present study recommends the use of font type Verdana for presentation of individual alphabets on various electronic displays in order to reduce cognitive workload.

A Comparison of Three Workload Methodologies: POP, IP/PCT, and POPIP

This paper examines three of the Integrated Performance Modelling Environment's built-in workload methodologies: Prediction of Operator Performance (POP), Information Processing/Perceptual Control Theory (IP/PCT), and POPIP. The newly-implemented POPIP is compared to its forerunner methodologies, POP and IP/PCT. POP, developed by QinetiQ, predicts performance degradation from interference between concurrent tasks using estimates of workload on different channels. IP/PCT, developed by DRDC Toronto, theorizes that all factors that impact human cognitive workload can be reduced to their effects on the amount of information to be processed, and the amount of time available before the task must be completed. POPIP uses components from both POP and IP/PCT for a combined workload algorithm that offers interference based on time pressure, and task scheduling. A sample IPME model is discussed, and used as an example in comparing all three workload methodologies.