Eye Movement Characteristics Research Articles

Correlations Between Oculometric Measures and Traditional Clinical Assessments in Multiple Sclerosis.

Oculomotor abnormalities are common in multiple sclerosis (MS) but are not quantitatively evaluated in clinical practice. Oculometric measures (OMs) are characteristics of eye movements captured while performing a visual task, e.g., the latency of saccadic and anti-saccadic metrics. Physical and cognitive deficits are prevalent among persons with MS, including disease-related oculomotor dysfunction. Recently, we have implemented a novel software-based platform enabling the extraction of OMs using a PC and a webcam. The objective of this study is to investigate the relationships between OMs and traditional outcome measures of physical and cognitive dysfunction in MS. Oculometric evaluation using a novel software-based platform (NeuraLight, Israel) was performed in patients with relapsing-remitting MS (n = 57; 36 females, age 41.4 ± 8.6). Physical disability was assessed by an MS-specific neurologic exam (Expanded Disability Status Scale; EDSS) and quantitative measures of cognitive and sensorimotor function (Symbol Digit Modalities Test; SDMT and Nine Hole Peg Test; NHPT). Various OMs were calculated out of multiple measures: Saccadic latency, gain and gaze during fixation, as well as error rate of saccades. Spearman's rank correlation was computed for each OM to assess the relationship with clinical scores. Various OMs were correlated with EDSS scores, as pro- and anti-saccadic latency (OM1 r = 0.36, OM2 r = 0.50, OM3 r = 0.39, OM4 r = 0.49; P-values<0.0001), initial gain during saccades (OM6 r = 0.47, OM7 r = 0.30, OM8 r = 0.59; p < 0.0001), stability of gaze during fixation (OM9 r = 0.48, OM10 r = 0.41; p < 0.0001) and error rate of anti-saccades (OM11 r = 0.59; p < 0.0001). Similar correlations were found between these OMs and NHPT scores (OM1 r = 0.41, OM2 r = 0.46, OM3 r = 0.31, OM4 r = 0.50; P-values<0.0001), initial gain (OM6 r = 0.40, OM7 r = 0.39, OM8 r = 0.58; Ps<0.0001) and error rate (OM11 r = 0.36; p < 0.0001). Finally, OMs were correlated with SDMT scores: OM1 and OM2 r=-0.31, OM3 r=-0.26, SD r=-0.38; p < 0.05), OM7 r=-0.36, OM8 r=-0.45; Ps<0.0001) and fixation stability (OM9 r=-0.36, Om10 r=-0.45; p < 0.05). OMs captured using a novel software-based platform were found to be associated with physical and cognitive function, suggesting that they can be used as a relevant tool in MS clinical assessment. Further studies will include larger cohorts and assess participants longitudinally to determine the potential value of OMs as predictors of future MS-related disability.

Apathy in Alzheimer's disease: Eye movements characteristics and neurostructural basis.

We sought to evaluate the characteristics of eye movements in Alzheimer's disease (AD) patients with apathy (AD-A) and their ability to identify AD-A and explore the shared neurostructure of eye movements and apathy. Total 32 normal controls, 36 AD-A and 72 AD with no apathy (AD-NA) patients were recruited. Parameters of smooth pursuit, fixation, prosaccade and antisaccade were compared among the three groups. Correlation analyses were conducted on apathy score, eye movement parameters and gray matter volume (GMV) in AD patients. Receiver operating characteristic curves were used to determine the power of eye movement parameters to identify AD-A patients. AD-A group exhibited a longer start-up duration of smooth pursuit, latency of gap and overlap prosaccades than AD-NA and normal control groups. In AD patients, apathy score was positively correlated with latency of overlap prosaccade. The GMVs of right pregenual anterior cingulate cortex (ACC) and left supracallosal ACC were negatively correlated with apathy score. Regions shared by apathy and latency of overlap prosaccade included right pregenual ACC and left supracallosal ACC. The area under curve discriminated AD-A from AD-NA patients by combining start-up duration, latency of gap and overlap prosaccade, and demographic information was 0.812. AD-A patients exhibit delayed initiation in eye movements, and the more prominent apathy indicates prolonged latency of overlap prosaccade in AD patients. Apathy and latency of overlap prosaccade share a neurostructural basis in AD patients. Our results contribute to providing a new method for early identification and severity assessment for AD-A.

Fixation-related potentials during a virtual navigation task: The influence of image statistics on early cortical processing.

Historically, electrophysiological correlates of scene processing have been studied with experiments using static stimuli presented for discrete timescales where participants maintain a fixed eye position. Gaps remain in generalizing these findings to real-world conditions where eye movements are made to select new visual information and where the environment remains stable but changes with our position and orientation in space, driving dynamic visual stimulation. Co-recording of eye movements and electroencephalography (EEG) is an approach to leverage fixations as time-locking events in the EEG recording under free-viewing conditions to create fixation-related potentials (FRPs), providing a neural snapshot in which to study visual processing under naturalistic conditions. The current experiment aimed to explore the influence of low-level image statistics-specifically, luminance and a metric of spatial frequency (slope of theamplitude spectrum)-on the early visual components evoked from fixation onsets in a free-viewing visual search and navigation task using a virtual environment. This research combines FRPs with an optimized approach to remove ocular artifacts and deconvolution modeling to correct for overlapping neural activity inherent in any free-viewing paradigm. The results suggest that early visual components-namely, the lambda response and N1-of the FRPs are sensitive to luminance and spatial frequency around fixation, separate from modulation due to underlying differences in eye-movement characteristics. Together, our results demonstrate the utility of studying the influence of image statistics on FRPs using a deconvolution modeling approach to control for overlapping neural activity and oculomotor covariates.

Diagnosis of Parkinson’s disease by eliciting trait-specific eye movements in multi-visual tasks

BackgroundParkinson’s Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.MethodsWe recruited 114 PD patients and 125 healthy controls and collected their eye-tracking data in a VR environment. Participants completed a series of specific VR tasks, including gaze stability, pro-saccades, anti-saccades, and smooth pursuit. After the tasks, eye movement features were extracted from the behaviors of fixations, saccades, and smooth pursuit to establish a PD diagnostic model.ResultsThe performance of the models was evaluated through cross-validation, revealing a recall of 97.65%, an accuracy of 92.73%, and a receiver operator characteristic area under the curve (ROC-AUC) of 97.08% for the proposed model.ConclusionWe extracted PD-specific eye movement features from the behaviors of fixations, saccades, and smooth pursuit in a VR environment to create a model with high accuracy and recall for PD diagnosis. Our method provides physicians with a new auxiliary tool to improve the prognosis and quality of life of PD patients.

Construction of a prediction model for Alzheimer’s disease using an AI-driven eye-tracking task on mobile devices

BackgroundEye-movement can reflect cognition and provide information on the neurodegeneration, such as Alzheimer’s disease (AD). The high cost and limited accessibility of eye-movement recordings have hindered their use in clinics.AimsWe aim to develop an AI-driven eye-tracking tool for assessing AD using mobile devices with embedded cameras.Methods166 AD patients and 107 normal controls (NC) were enrolled. The subjects completed eye-movement tasks on a pad. We compared the demographics and clinical features of two groups. The eye-movement features were selected using least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) model was trained to classify AD and NC, and its performance was evaluated. A nomogram was established to predict AD.ResultsIn training set, the model showed a good area under curve (AUC) of 0.85 for identifying AD from NC, with a sensitivity of 71%, specificity of 84%, positive predictive value of 0.87, and negative predictive value of 0.65. The validation of the model also yielded a favorable discriminatory ability with the AUC of 0.91, sensitivity, specificity, positive predictive value, and negative predictive value of 82%, 91%, 0.93, and 0.77 to identify AD patients from NC.Discussion and ConclusionsThis novel AI-driven eye-tracking technology has the potential to reliably identify differences in eye-movement abnormalities in AD. The model shows excellent diagnostic performance in identifying AD based on the current data collected. The use of mobile devices makes it accessible for AD patients to complete tasks in primary clinical settings or follow up at home.

Метод динамической оценки утомления оператора ПК на основе характеристик глазных движений

The onset of fatigue is dangerous in areas of activity that require high concentration of human attention, such as air traffic controllers, nuclear power plant operators, etc. It should be noted that these types of activities are characterized by the fact that most of the time the employee sits at the workplace and his gaze is directed at the monitor. The paper presents a method for dynamic detection of PC operator fatigue based on eye movement characteristics. The method for dynamic detection of fatigue implements a training scenario for a fatigue detection model and a fatigue detection scenario. Within the training scenario, eye movement characteristics are calculated and correlations with fatigue test results are searched for. Within the fatigue detection scenario, eye movement characteristics that most strongly correlate with fatigue are selected. These characteristics can also be divided by the types of physical events on which they are based. We can distinguish such characteristics as speed, time, quantity, size, percentage, frequency and ratio characteristics. To find correlations between eye movement characteristics and fatigue, a dataset of eye movements and the results of tests and questionnaires such as the go/no-go task, the Landolt ring test, and the VAS-F questionnaire were analyzed. The dataset consists of gaze coordinate recordings from 15 participants acting as PC operators. To determine the degree of fatigue, the participant completed the VAS-F questionnaire. The Landolt ring correction test is a test used to measure attention concentration. The labeled dataset is used to train a machine learning model that detects fatigue. The experimental results showed that using the characteristics selected in the study yielded the most promising results. This approach allowed us to achieve the highest F-measure and the best average accuracy, indicating the overall reliability of the model.

Age- and Sex-Based Developmental Biomarkers in Eye Movements.

Eye movement research serves as a critical tool for assessing brain function, diagnosing neurological and psychiatric disorders, and understanding cognition and behavior. Sex differences have largely been under reported or ignored in neurological research. However, eye movement features provide biomarkers that are useful for disease classification with superior accuracy and robustness compared to previous classifiers for neurological diseases. Neurological diseases have a sex specificity, yet eye movement analysis has not been specific to our understanding of sex differences. The study involved subjects recruited from 804 sites equipped with RightEye Vision Systems, primarily located in optometry practices across the United States. Subjects completed six eye movement assessments: circular smooth pursuit (CSP), horizontal smooth pursuit (HSP), vertical smooth pursuit (VSP), horizontal saccades (HS), vertical saccades (VS), and fixation stability (FS). Eye movements were analyzed and classified in accordance with age and sex by multiple t-tests and linear regression models. This study represented a large sample size of 23,557 subjects, with 11,871 males and 11,686 females representing ages from birth through 80 years of age. We observed statistically significant differences for all eye movement functions between males and females. We demonstrate that eye movements are sex-specific and offer normative data to compare sex-specific eye movement function by age. Novel baseline metrics can be compared to individual performance, regardless of sex. This study represents significant progress in linking eye movements with brain function and clinical syndromes, allowing researchers and clinicians to stratify individuals by age and sex.

Impact of access point density on driving safety in underground loops: Examining drivers’ operational performance and visual characteristics

Underground loops, which are characterized by multiple access points (APs) and an enclosed environment, present unique driving safety challenges. This study investigated the impact of AP densities on driving safety in underground loops. We conducted field tests utilizing four AP conditions (7, 9, 12, and 14 APs/km) and collected data on drivers’ operational performance and visual characteristics. To analyze the differences in driving behavior across these conditions, one-way repeated measures ANOVA was applied. Additionally, a comprehensive evaluation model based on matter-element extension analysis was developed to evaluate the impact of AP density on driving safety. The results indicated that AP density significantly affects acceleration variations and eye movement characteristics. As the density of APs increased, drivers demonstrated improved speed control and enhanced driving stability. However, this also resulted in a wider visual search range, increased visual recognition difficulty, and a higher mental workload. The comprehensive evaluation revealed that under 7 APs/km conditions, the overall evaluation grade was “Good,” indicating better driving safety. In contrast, the 14 APs/km condition received a grade of “Poor,” suggesting a higher driving risk. These findings provide guidance for transportation management agencies aiming to improve driving safety in underground loops.

Exploring Eye Movement Features of Motion Sickness Using Closed-Track Driving Experiments.

To explore and validate effective eye movement features related to motion sickness (MS) through closed-track experiments and to provide valuable insights for practical applications. With the development of autonomous vehicles (AVs), MS has attracted more and more attention. Eye movements have great potential to evaluate the severity of MS as an objective quantitative indicator of vestibular function. Eye movement signals can be easily and noninvasively collected using a camera, which will not cause discomfort or disturbance to passengers, thus making it highly applicable. Eye movement data were collected from 72 participants susceptible to MS in closed-track driving environments. We extracted features including blink rate (BR), total number of fixations (TNF), total duration of fixations (TDF), mean duration of fixations (MDF), saccade amplitude (SA), saccade duration (SD), and number of nystagmus (NN). The statistical method and multivariate long short-term memory fully convolutional network (MLSTM-FCN) were used to validate the effectiveness of eye movement features. Significant differences were shown in the extracted eye movement features across different levels of MS through statistical analysis. The MLSTM-FCN model achieved an accuracy of 91.37% for MS detection and 88.51% for prediction in binary classification. For ternary classification, it achieved an accuracy of 80.54% for MS detection and 80.11% for prediction. Evaluating MS through eye movements is effective. The MLSTM-FCN model based on eye movements can efficiently detect and predict MS. This work can be used to provide a possible indication and early warning for MS.

The association between eye movement characteristics and cognitive function in adolescents with major depressive disorder

ObjectiveThis study aims to explore the relationship between eye movement characteristics and cognitive function in adolescents with major depressive disorder (MDD). MethodEyeLink 1000 eye tracker was used to obtain eye movement data in free-viewing and smooth pursuit tasks. Chi-square test and Mann-Whitney test were used for inter-group comparison of demographic and clinical data. Spearman correlation was used to analyze the correlation between eye movement characteristics and cognitive function. ResultsAdolescents with MDD showed lower saccade amplitude in the free-viewing task and more fixations and saccades in the smooth pursuit task. In the free-viewing task, fixation count, saccade duration and saccade speed were found to be positively correlated with immediate memory and attention; fixation duration was negatively correlated with immediate memory. In the smooth pursuit task, saccade count was positively correlated with the faux pas test; fixation duration and saccade duration were significantly correlated with memory and attention. ConclusionAdolescents with MDD showed abnormalities in several indices of eye movement, and altered eye movement variables were also correlated with cognitive deficits. Eye-tracking technology helps illustrate the diverse cognitive strategies employed by individuals during cognitive tasks, allowing researchers to explore subtle differences in cognitive processes.

Analysis of eye movement features in patients with Alzheimer's disease based on intelligent eye movement analysis and evaluation system.

The early identification of Alzheimer's disease (AD) benefits patients, so creating a simple and convenient method is crucial for diagnosing early symptoms. To offer a potential approach for the early detection of both AD and mild cognitive impairment (MCI). Eye movement data from 66 patients were divided into three groups, including healthy control group (HC), MCI group, and AD group. We searched for parameters that can detect MCI at an early stage and drew receiver operating characteristic (ROC) curves. The correlation between eye movement parameters and cognitive scores was analyzed. The MCI group differed from the HC group in error correction rate of antisaccade (p = 0.008) and total offset degrees (>4°) (p = 0.011) of lateral fixation. The AD group had different overlap prosaccade accuracy (p = 0.025), latency (p = 0.009) and average completion time (p = 0.015), gap prosaccade latency (p = 0.005) and average completion time (p = 0.005), antisaccade accuracy (p = 0.006), error correction rate (p < 0.001) and average saccade velocity (p = 0.035), and lateral fixation accuracy (p = 0.018), total offset degrees (>4°) (p = 0.041) compared to the HC group. The AD group differed significantly from the MCI group in accuracy (p = 0.001) and error correction rate (p = 0.044) of antisaccades, the latency (p = 0.009) and average completion time (p = 0.025) of overlap prosaccade and the latency (p = 0.038) of gap prosaccade, these parameters can serve as indicators to monitor the progress of the disease. Lateral fixation combined with antisaccade was more conducive to identifying MCI patients with the area under the ROC curve of 0.837. Most eye movement parameters had a light to moderate correlation with cognitive scores. Eye movements can be used for early identification of MCI/AD patients and to monitor disease progression.

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression.

Depression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis. We recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals. Compared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition. This study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.

Concurrent oculomotor hyperactivity and deficient anti-saccade performance in obsessive-compulsive disorder

Existing studies mainly focused on the inhibition of the task-interfering response to understand the inhibitory deficits of obsessive-compulsive disorder (OCD). However, recent studies suggested that inhibitory function is broadly involved in response preparation and implementation. It is yet unknown if the inhibition dysfunction in OCD extends beyond the task-interfering response to the general inhibitory function. Here we address this issue based on the multidimensional eye-movement measurements, which can better capture the inhibitory deficits than manual responses. Thirty-one OCD patients and 32 healthy controls (HCs) completed the anti-saccade task where multidimensional eye-movement features were developed. Confirmatory factor analysis (CFA) suggested two components of inhibitory function that negatively correlated with each other: one component of oculomotor hyperactivity in generating oculomotor output which is characterized with early premature saccades, early cross rates and saccade number; the other component of task-specific oculomotor efficiency which is characterized with task accuracy, saccade latency, correction rate, and amplitude gain. Importantly, OCD showed both stronger oculomotor hyperactivity and deficient oculomotor efficiency than HCs, and the machine-learning-based classifications showed that the features of oculomotor hyperactivity had higher prediction accuracy than the features of oculomotor efficiency in distinguishing OCD from HCs. Our results suggested that OCD has concurrent deficits in oculomotor hyperactivity and oculomotor efficiency, which may originate from a common inhibitory dysfunction.

Effect of Stimulus Regularities on Eye Movement Characteristics

Humans have the unique ability to discern spatial and temporal regularities in their surroundings. However, the effect of learning these regularities on eye movement characteristics has not been studied enough. In the present study, we investigated the effect of the frequency of occurrence and the presence of common chunks in visual images on eye movement characteristics like the fixation duration, saccade amplitude and number, and gaze number across sequential experimental epochs. The participants had to discriminate the patterns presented in pairs as the same or different. The order of pairs was repeated six times. Our results show an increase in fixation duration and a decrease in saccade amplitude in the sequential epochs, suggesting a transition from ambient to focal information processing as participants acquire knowledge. This transition indicates deeper cognitive engagement and extended analysis of the stimulus information. Interestingly, contrary to our expectations, the saccade number increased, and the gaze number decreased. These unexpected results might imply a reduction in the memory load and a narrowing of attentional focus when the relevant stimulus characteristics are already determined.

Eye-movement-prompted large image captioning model

Pretrained large vision-language models have shown outstanding performance on the task of image captioning. However, owing to the insufficient decoding of image features, existing large models sometimes lose important information, such as objects, scenes, and their relationships. In addition, the complex “black-box” nature of these models makes their mechanisms difficult to explain. Research shows that humans learn richer representations than machines do, which inspires us to improve the accuracy and interpretability of large image captioning models by combining human observation patterns. We built a new dataset, called saliency in image captioning (SIC), to explore relationships between human vision and language representation. One thousand images with rich context information were selected as image data of SIC. Each image was annotated with five caption labels and five eye-movement labels. Through analysis of the eye-movement data, we found that humans efficiently captured comprehensive information for image captioning during their observations. Therefore, we propose an eye-movement-prompted large image captioning model, which is embedded with two carefully designed modules: the eye-movement simulation module (EMS) and the eye-movement analyzing module (EMA). EMS combines the human observation pattern to simulate eye-movement features, including the positions and scan paths of eye fixations. EMA is a graph neural network (GNN) based module, which decodes graphical eye-movement data and abstracts image features as a directed graph. More accurate descriptions can be predicted by decoding the generated graph. Extensive experiments were conducted on the MS-COCO and NoCaps datasets to validate our model. The experimental results showed that our network was interpretable, and could achieve superior results compared with state-of-the-art methods, i.e., 84.2% BLEU-4 and 145.1% CIDEr-D on MS-COCO Karpathy test split, indicating its strong potential for use in image captioning.

Cross-Modal Guiding Neural Network for Multimodal Emotion Recognition From EEG and Eye Movement Signals.

Multimodal emotion recognition research is gaining attention because of the emerging trend of integrating information from different sensory modalities to improve performance. Electroencephalogram (EEG) signals are considered objective indicators of emotions and provide precise insights despite their complex data collection. In contrast, eye movement signals are more susceptible to environmental and individual differences but offer convenient data collection. Conventional emotion recognition methods typically use separate models for different modalities, potentially overlooking their inherent connections. This study introduces a cross-modal guiding neural network designed to fully leverage the strengths of both modalities. The network includes a dual-branch feature extraction module that simultaneously extracts features from EEG and eye movement signals. In addition, the network includes a feature guidance module that uses EEG features to direct eye movement feature extraction, reducing the impact of subjective factors. This study also introduces a feature reweighting module to explore emotion-related features within eye movement signals, thereby improving emotion classification accuracy. The empirical findings from both the SEED-IV dataset and our collected dataset substantiate the commendable performance of the model, thereby confirming its efficacy.

Detecting five-pattern personality traits using eye movement features for observing emotional faces.

The five-pattern personality traits rooted in the theory of traditional Chinese medicine (TCM) have promising prospects for clinical application. However, they are currently assessed using a self-report scale, which may have certain limitations. Eye tracking technology, with its non-intrusive, objective, and culturally neutral characteristics, has become a powerful tool for revealing individual cognitive and emotional processes. Therefore, applying this technology for personality assessment is a promising approach. In this study, participants observed five emotional faces (anger, happy, calm, sad, and fear) selected from the Chinese Facial Affective Picture System. Utilizing artificial intelligence algorithms, we evaluated the feasibility of automatically identifying different traits of the five-pattern personality traits from participants' eye movement patterns. Based on the analysis of five supervised learning algorithms, we draw the following conclusions: The Lasso feature selection method and Logistic Regression achieve the highest prediction accuracy for most of the traits (TYa, SYa, SYi, TYi). This study develops a framework for predicting five-pattern personality traits using eye movement behavior, offering a novel approach for personality assessment in TCM.

Related work analysis for determination of fatigue state based on eye movements monitoring

We have reviewed theoretical background of detecting functional state of fatigue based on the strategy of eye movements. Also, modern methods for assessing eye movements were considered. Based on our literature review, we can conclude that nowadays there are multitude numerical characteristics of eye movements, the dynamics of which can hypothetically make it possible to assess degree of fatigue. However, there are still no proposals for a method for determining the degree of fatigue based on an analysis of the strategy of eye movements. In this regard, according to the concepts of static and dynamic vision, it is proposed to consider the shift in the numerical characteristics of eye movements towards characteristics that reflect the strategy of dynamic vision as evidence of fatigue.

Eye Movement Evaluation of Pedestrians' Mobile Phone Usage at Street Crossings

Vision is one of the most important human senses, accounting for most of the external information pedestrians receive while crossing the street. However, distracted mobile phone usage during street crossing consumes pedestrians’ cognitive resources and diverts their visual attention. As a result, pedestrians may be unable to fully concentrate on observing the traffic environment and effectively planning their crossing path and behavior. This study evaluated the effect of pedestrian behavioral activities at street crossings on eye-movement (EM) characteristics. The crossing tasks were natural behavior, voice call, text messaging, and listening to music. The tasks were further categorized as simple or complex. A total of 29 participants were recruited in Nanjing: 18 males (62.1%) and 11 females (37.9%) with an average age of 23.59 years (SD = 2.44). The Friedman test was used to analyze differences in saccade frequency, fixation time, browsing number, and browsing time across different scenarios. Text messaging had the most significant impact on pedestrians’ EM characteristics, followed by voice call; music listening had a relatively weaker effect. Secondary task difficulty influenced the percentage of browsing, viewing, and to some extent gaze time. On the other hand, music rhythm and style only partially influenced the percentage of gaze and gaze time. Mobile phones substantially affected pedestrians’ EM characteristics and attention allocation for the same level of secondary task difficulty. These findings contribute to a better understanding of pedestrians’ visual characteristics under distracted mobile phone usage conditions and provide valuable insights for developing appropriate measures to enhance pedestrian safety.