Estimation of cognitive load based on the pupil size dilation

Gavas, Rahul; Chatterjee, Debatri; Sinha, Aniruddha

doi:10.1109/smc.2017.8122826

Cited by 39 publications

(15 citation statements)

References 31 publications

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“…Literature review indicates that often pupil size can be representative of one’s awareness or metacognitive confidence with which an individual is performing a task [ 33 , 24 ]. Also, the effects of cognitive load can also be quantified by studying spontaneous pupillary responses [ 23 ]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Among the different modalities of picking up one’s response to a task, eye tracking is one of the promising modalities in the domain of cognitive performance analysis. Gaze analysis along with pupil response provides valuable insights of the cognitive workload [ 23 ], confidence [ 24 ] and decision making [ 25 ] abilities of an individual. Nicolas et al [ 26 ] had shown the possibility of emotion analysis using fixation duration of the eye for depressed older adults and normal controls.…”

Section: Related Workmentioning

confidence: 99%

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Evaluating age-related variations of gaze behavior for a novel digitized-digit symbol substitution test

Chatterjee¹,

Gavas²,

Chakravarty³

et al. 2019

JEMR

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Analysis of cognitive functioning from gaze behavior might serve as an early indicator of age related decline of cognitive functions. Standard psychological tests like the digit-symbol substitution test or the symbol-digit modalities test is used exclusively in this regard. In this paper, we have designed and developed a digitized version of the digit symbol substitution test. Three different versions have been designed in order to derive deeper insights of the user behavior. The test-retest validation of the versions reveals good correlation across sessions. Fur-ther, the difference in gaze behavior which might be used as an indicator of cognitive functions is tested for two different age groups (13 participants<30 years and 11 participants >40 years). It is seen that the designed digitized version along with the usage of physiological markers like eye tracking bestows additional information and is sensitive to age related factors which might be used for the assessment as well as for the training purpose in rehabilitation systems. Results show that the performance can be analyzed using gaze and pupillometric features in addition to the conventional test performance metrics. We derived an index to measure the performance related to visuo-spatial functioning on one of the de-signed versions of the test. Results of this index on the number of fixations for two age groups are found to be separated in a statistically significant (p<0.05) manner. The age related difference (p<0.05) is also evident in the pupillometric responses obtained.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evaluating age-related variations of gaze behavior for a novel digitized-digit symbol substitution test

Chatterjee¹,

Gavas²,

Chakravarty³

et al. 2019

JEMR

Self Cite

View full text Add to dashboard Cite

show abstract

“…These results suggest that the occurrence of dementia is associated to reduced syntactic complexity, difficulty in connecting one event to the next, in maintaining the theme, and in understanding the story. Furthermore, grammatical errors have mainly been observed in severe AD groups (Jarrold et al, 2014). Some semantic features seem to be relevant for MCI though.…”

Section: Speech and Languagementioning

confidence: 98%

“…When comparing facial muscles and eye movement, less variability is seen for AD patients compared to healthy controls (Nam et al, 2020). Pupil dilation is a robust predictor of cognitive load, the working memory demands of performing a certain task (Gavas et al, 2017). Pupillary response, mainly in terms of changes in reaction to light, has been proposed as a biomarker of early stages for Alzheimer's disease (Granholm et al, 2017), However, a longitudinal study with AD biomarkers is needed to confirm whether pupillary responses can provide a predictive biomarker of risk specific to AD-related declines.…”

Section: Gaze and Pupil Dilationmentioning

confidence: 99%

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

Jonell

Moëll

Håkansson

et al. 2021

Front. Comput. Sci.

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Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.

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“…Researchers also found that the change in pupil dilation is related to change in the viewing of angles of the photograph (Hess 1975). Recent researchers (Gavas 2017;Duchowski 2018) have used a metric to detect cognitive load by measuring frequency and power of pupil dilation. Gavas, as well as Duchowski, have used chin rest for the experiment to control head movements which makes the system difficult to realize in real-time situations.…”

Section: Introductionmentioning

confidence: 99%

Comparing Pupil Dilation, Head Movement, and EEG for Distraction Detection of Drivers

Prabhakar

Madhu

Biswas

2018

Electronic Workshops in Computing

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This paper investigates the use of pupil dilation, head movement and EEG for detecting distraction and cognitive load of drivers while performing secondary tasks in an automotive environment. We tracked pupil dilation from Tobii Pro Glasses 2, head movement from Kinect and EEG from Emotive Insight system. We have analyzed data using Fast Fourier Transform, Continuous Wavelet Transform, and Discrete Wavelet Transform for the full-length signal as well as in windows of 1 second for real-time implementation. We investigated detection of distraction and cognitive load from three different conditions-free driving, driving with lane change, driving with lane change and operating secondary task for each participant in a driving simulator. Our results show that the pupil dilation, head yaw, and EEG can detect the increase in cognitive load due to operation of secondary task within a time buffer of 1 second which can be adapted for real-time implementation. We have also found that FFT of Pupil dilation shows significant categorization of normal and distracted states than the categorization by DWT which contrasts with state of the art methods. Finally, we have proposed an expert system to alert drivers utilizing the signal processing analysis.

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Estimation of cognitive load based on the pupil size dilation

Cited by 39 publications

References 31 publications

Evaluating age-related variations of gaze behavior for a novel digitized-digit symbol substitution test

Evaluating age-related variations of gaze behavior for a novel digitized-digit symbol substitution test

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

Comparing Pupil Dilation, Head Movement, and EEG for Distraction Detection of Drivers

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