Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment

Haque, Rafi U.; Pongos, Alvince L.; Manzanares, Cecelia; Lah, James J.; Levey, Aĺlan I.; Clifford, Gari D.

doi:10.1109/tbme.2020.2990734

Cited by 36 publications

(43 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The details of the Visuospatial Memory Eyetracking Test was described in [24,25], a passive viewing test that asks participants to enjoy the images displayed on the screen, where the participants were not asked to perform any memorizing task and did not get scores or any kind of feedback during the test. In short, the task first shows 20 images of scenes consisting of two to five objects for a duration of five seconds, then displays a modified set of images with either one object added or removed from each image.…”

Section: Data Collectionmentioning

confidence: 99%

“…The memory test was administered to the participants using the same protocol described in [25]. Briefly, the test was presented on an iPad Air 9.7" tablet with maximum screen brightness and mounted on a stand in portrait orientation during the test.…”

Section: Plos Onementioning

confidence: 99%

“…We followed "Method 4" proposed in [25], in which we implemented a deep learning-based eye-tracking method to estimate the viewing time of the modified region in an image. The viewing time was defined as the percentage of frames (excluding the ones without face/eyes detected) where the estimated gaze is in a fixed expert-defined elliptical region (for each object/picture).…”

Section: Vision-based Eye Trackingmentioning

confidence: 99%

See 2 more Smart Citations

Automated analysis of facial emotions in subjects with cognitive impairment

et al. 2022

Self Cite

View full text Add to dashboard Cite

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer’s diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Vision-based Eye Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Automated analysis of facial emotions in subjects with cognitive impairment

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The features of the sources and target areas are mapped into the meta-feature space. As displayed in [19,20], the procedure begins with source annotated data from which the action formats are recovered. The activity layouts show that timestamp, location, and context.…”

Section: Related Workmentioning

confidence: 99%

Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Ghayvat

Gope

2021

Neural Comput & Applic

View full text Add to dashboard Cite

Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.

show abstract

“…Signal classification is the basis for identifying the diverse movement states of eyeballs. Various algorithms have emerged in the past decade, including the Hidden Markov Model (HMM) [ 30 , 31 , 32 , 33 ], transfer learning [ 34 , 35 , 36 , 37 ], and linear classifiers [ 38 , 39 , 40 , 41 ]. A hierarchical HMM statistical algorithm [ 33 ] was utilized to classify ternary eye movements and the classification result of fixations, saccades and smooth pursuits was evaluated as “good”.…”

Section: Introductionmentioning

confidence: 99%

Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM

et al. 2021

View full text Add to dashboard Cite

To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user’s forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young’s modulus (286 KPa) and exceptional breathability (18 g m−2 h−1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.

show abstract

Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment

Cited by 36 publications

References 15 publications

Automated analysis of facial emotions in subjects with cognitive impairment

Automated analysis of facial emotions in subjects with cognitive impairment

Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM

Contact Info

Product

Resources

About