SmartEye: Developing a Novel Eye Tracking System for Quantitative Assessment of Oculomotor Abnormalities

Wearable Near-Eye Tracking Technologies for Health: A Review

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human-computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity. These features have drawn considerable attention in the health domain, as NET provides accessible solutions for long-term and continuous health monitoring and a comfortable and interactive user interface. Herein, this work offers an inaugural concise review of NET for health, encompassing approximately 70 related articles published over the past two decades and supplemented by an in-depth examination of 30 literatures from the preceding five years. This paper provides a concise analysis of health-related NET technologies from aspects of technical specifications, data processing workflows, and the practical advantages and limitations. In addition, the specific applications of NET are introduced and compared, revealing that NET is fairly influencing our lives and providing significant convenience in daily routines. Lastly, we summarize the current outcomes of NET and highlight the limitations.

Eye tracking technology in medical practice: a perspective on its diverse applications

Eye tracking technology has emerged as a valuable tool in the field of medicine, offering a wide range of applications across various disciplines. This perspective article aims to provide a comprehensive overview of the diverse applications of eye tracking technology in medical practice. By summarizing the latest research findings, this article explores the potential of eye tracking technology in enhancing diagnostic accuracy, assessing and improving medical performance, as well as improving rehabilitation outcomes. Additionally, it highlights the role of eye tracking in neurology, cardiology, pathology, surgery, as well as rehabilitation, offering objective measures for various medical conditions. Furthermore, the article discusses the utility of eye tracking in autism spectrum disorders, attention-deficit/hyperactivity disorder (ADHD), and human-computer interaction in medical simulations and training. Ultimately, this perspective article underscores the transformative impact of eye tracking technology on medical practice and suggests future directions for its continued development and integration.

Real-time monitoring of driver distraction: State-of-the-art and future insights

Driver distraction and inattention have been found to be major contributors to a large number of serious road crashes. It is evident that distraction reduces to a great extent driver perception levels as well as their decision making capability and the ability of drivers to control the vehicle. An effective way to mitigate the effects of distraction on crash probability, would be through monitoring the mental state of drivers or their driving behaviour and alerting them when they are in a distracted state. Towards that end, in recent years, several inexpensive and effective detection systems have been developed in order to cope with driver inattention. This study endeavours to critically review and assess the state-of-the-art systems and platforms measuring driver distraction or inattention. A thorough literature review was carried out in order to compare and contrast technologies that can be used to detect, monitor or measure driver's distraction or inattention. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results indicated that in most of the identified studies, driver distraction was measured with respect to its impact to driver behaviour. Real-time eye tracking systems, cardiac sensors on steering wheels, smartphone applications and cameras were found to be the most frequent devices to monitor and detect driver distraction. On the other hand, less frequent and effective approaches included electrodes, hand magnetic rings and glasses.

Pandemic disease detection through wireless communication using infrared image based on deep learning

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method.

An Effective Algorithm to Analyze the Optokinetic Nystagmus Waveforms from a Low-Cost Eye Tracker

Objective: Most neurological diseases are usually accompanied by changes in the oculomotor nerve. Analysis of different types of eye movements will help provide important information in ophthalmology, neurology, and psychology. At present, many scholars use optokinetic nystagmus (OKN) to study the physiological phenomenon of eye movement. OKN is an involuntary eye movement induced by a large moving surrounding visual field. It consists of a slow pursuing eye movement, called “slow phase” (SP), and a fast re-fixating saccade eye movement, called “fast phase” (FP). Non-invasive video-oculography has been used increasingly in eye movement research. However, research-grade eye trackers are often expensive and less accessible to most researchers. Using a low-cost eye tracker to quantitatively measure OKN eye movement will facilitate the general application of eye movement research. Methods & Results: We design an analytical algorithm to quantitatively measure OKN eye movements on a low-cost eye tracker. Using simple conditional filtering, accurate FP positions can be obtained quickly. The high-precision FP recognition rate is of great help for the subsequent calculation of eye movement analysis parameters, such as mean slow phase velocity (MSPV), which is beneficial as a reference index for patients with strabismus and other eye diseases. Conclusions: Experimental results indicate that the proposed method achieves faster and better results than other approaches, and can provide an effective algorithm to calculate and analyze the FP position of OKN waveforms.

Portable Neuroimaging-Guided Noninvasive Brain Stimulation of the Cortico-Cerebello-Thalamo-Cortical Loop—Hypothesis and Theory in Cannabis Use Disorder

Background: Maladaptive neuroplasticity-related learned response in substance use disorder (SUD) can be ameliorated using noninvasive brain stimulation (NIBS); however, inter-individual variability needs to be addressed for clinical translation. Objective: Our first objective was to develop a hypothesis for NIBS for learned response in SUD based on a competing neurobehavioral decision systems model. The next objective was to develop the theory by conducting a computational simulation of NIBS of the cortico-cerebello-thalamo-cortical (CCTC) loop in cannabis use disorder (CUD)-related dysfunctional “cue-reactivity”—a construct closely related to “craving”—that is a core symptom. Our third objective was to test the feasibility of a neuroimaging-guided rational NIBS approach in healthy humans. Methods: “Cue-reactivity” can be measured using behavioral paradigms and portable neuroimaging, including functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) metrics of sensorimotor gating. Therefore, we conducted a computational simulation of NIBS, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) of the cerebellar cortex and deep cerebellar nuclei (DCN) of the CCTC loop for its postulated effects on fNIRS and EEG metrics. We also developed a rational neuroimaging-guided NIBS approach for the cerebellar lobule (VII) and prefrontal cortex based on a healthy human study. Results: Simulation of cerebellar tDCS induced gamma oscillations in the cerebral cortex, while transcranial temporal interference stimulation induced a gamma-to-beta frequency shift. A preliminary healthy human study (N = 10) found that 2 mA cerebellar tDCS evoked similar oxyhemoglobin (HbO) response in the range of 5 × 10⁻⁶ M across the cerebellum and PFC brain regions (α = 0.01); however, infra-slow (0.01–0.10 Hz) prefrontal cortex HbO-driven phase–amplitude-coupled (PAC; 4 Hz, ±2 mA (max)) cerebellar tACS evoked HbO levels in the range of 10⁻⁷ M that were statistically different (α = 0.01) across these brain regions. Conclusion: Our healthy human study showed the feasibility of fNIRS of cerebellum and PFC and closed-loop fNIRS-driven ctACS at 4 Hz, which may facilitate cerebellar cognitive function via the frontoparietal network. Future work needs to combine fNIRS with EEG for multi-modal imaging for closed-loop NIBS during operant conditioning.

Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review

Many children have undetected vision problems or insufficient visual information processing that may be a factor in lower academic outcomes. The aim of this paper is to contribute to a better understanding of the importance of vision screening for school-aged children, and to investigate the possibilities of how eye-tracking (ET) technologies can support this. While there are indications that these technologies can support vision screening, a broad understanding of how to apply them and by whom, and if it is possible to utilize them at schools, is lacking. We review interdisciplinary research on performing vision investigations, and discuss current challenges for technology support. The focus is on exploring the possibilities of ET technologies to better support screening and handling of vision disorders, especially by non-vision experts. The data orginate from a literature survey of peer-reviewed journals and conference articles complemented by secondary sources, following a rapid review methodology. We highlight current trends in supportive technologies for vision screening, and identify the involved stakeholders and the research studies that discuss how to develop more supportive ET technologies for vision screening and training by non-experts.

The Digital Neurologic Examination

Digital health has been rapidly thrust into the forefront of care delivery. Poised to extend the clinician's reach, a new set of examination tools will redefine neurologic and neurosurgical care, serving as the basis for the digital neurologic examination. We describe its components and review specific technologies, which move beyond traditional video-based telemedicine encounters and include separate digital tools. A future suite of these clinical assessment technologies will blur the lines between history taking, examination, and remote monitoring. Prior to full-scale implementation, however, much more investigation is needed. Because of the nascent state of the technologies, researchers, clinicians, and developers should establish digital neurologic examination requirements in order to maximize its impact.

A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a "slow phase (SP)" with eye movements in the same direction as the movement of the pattern and a "fast phase (FP)" with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

Analysis of Facial Information for Healthcare Applications: A Survey on Computer Vision-Based Approaches

This paper gives an overview of the cutting-edge approaches that perform facial cue analysis in the healthcare area. The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g., the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING

Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.

Medical Diagnosis of Cerebral Palsy Rehabilitation Using Eye Images in Machine Learning Techniques

Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (misalignment of eye). The Eye image are captured through camera, this make the quick diagnosis and examination the periodical assessment for CP kids. By capturing the Eye Movement of 40 children with CP (aged 3-11 years) with relatively mild motor-impairment and also we have analyzed the performance of CP children periodically. Nowadays, Bio-Medical image processing and Machine learning Classification algorithm used for detection and diagnosis the certain diseases and plays the important tool to decrease the risk of any diseases. This work presents a computational methodology to automatically diagnose the Improvement of CP children and performance can be evaluated. The alternate medical evaluation techniques have shown their potential for the treatment and diagnosis of disease like strabismus and nystagmus for CP kids. The proposed method is used to measure and quantify the performance improvement by classify the abnormal eye condition of CP kids and these results attained by machine learning method. The results show the best classification accuracy of 94.17% calculated from Neural Network Classifier. Specificity Rate were absorbed as 0.9800 and Sensitivity Rate were absorbed as 0.9165 respectively. The proposed method for non-invasive and automatic detection of abnormalities in CP kids and evaluates the performance improvement more accurately.

Affordable sensor based gaze tracking for realistic psychological assessment

Eye movement analysis finds tremendous usefulness in various medical screening applications and rehabilitation. Infrared sensor based eye trackers are becoming popular but these are expensive and need repeated calibration. Moreover, with multiple calibration also, there persists some noises called, variable and systematic, resulting in inaccurate gaze tracking. This study aims to build an one time calibration module to avoid the overhead of multiple calibration and to design an algorithm to remove both the types of errors effectively. The proposed approach is used for correcting the gaze tracking data for Digit Gazing task and standard recall-recognition test, where an accuracy of 90% and 82% are achieved respectively for detecting the gaze positions against the raw eye gaze data. Results also show that it is possible to perform accurate gaze tracking with one-time calibration method provided the experimental setup is not altered.

Systems Analysis of Human Visuo-Myoelectric Control Facilitated by Anodal Transcranial Direct Current Stimulation in Healthy Humans

Induction of neuroplasticity by transcranial direct current stimulation (tDCS) applied to the primary motor cortex facilitates motor learning of the upper extremities in healthy humans. The impact of tDCS on lower limb functions has not been studied extensively so far. In this study, we applied a system identification approach to investigate the impact of anodal transcranial direct current stimulation of the leg area of the motor cortex via the human visuo-myoelectric controller. The visuo-myoelectric reaching task (VMT) involves ballistic muscle contraction after a visual cue. We applied a black box approach using a linear ARX (Auto-regressive with eXogenous input) model for a visuomotor myoelectric reaching task. We found that a 20th order finite impulse response (FIR) model captured the TARGET (single input)—CURSOR (single output) dynamics during a VMT. The 20th order FIR model was investigated based on gain/phase margin analysis, which showed a significant (p < 0.01) effect of anodal tDCS on the gain margin of the VMT system. Also, response latency and the corticomuscular coherence (CMC) time delay were affected (p < 0.05) by anodal tDCS when compared to sham tDCS. Furthermore, gray box simulation results from a Simplified Spinal-Like Controller (SSLC) model demonstrated that the input-output function for motor evoked potentials (MEP) played an essential role in increasing muscle activation levels and response time improvement post-tDCS when compared to pre-tDCS baseline performance. This computational approach can be used to simulate the behavior of the neuromuscular controller during VMT to elucidate the effects of adjuvant treatment with tDCS.

Towards Optimization of Gaze-Controlled Human-Computer Interaction: Application to Hindi Virtual Keyboard for Stroke Patients

Virtual keyboard applications and alternative communication devices provide new means of communication to assist disabled people. To date, virtual keyboard optimization schemes based on script-specific information along with multimodal input access facility are limited. In this work, we propose a novel method for optimizing the position of the displayed items for gaze-controlled tree-based menu selection systems by considering a combination of letter frequency and command selection time. The optimized graphical user interface (GUI) layout has been designed for a Hindi language virtual keyboard based on a menu wherein 10 commands provide access to type 88 different characters along with additional text editing commands. The system can be controlled in two different modes: eye-tracking alone and eye-tracking with an access soft-switch. Five different keyboard layouts have been presented and evaluated with ten healthy participants. Further, the two best performing keyboard layouts have been evaluated with eye-tracking alone on ten stroke patients. The overall performance analysis demonstrated significantly superior typing performance, high usability (87% SUS score), and low workload (NASA TLX with 17 scores) for the letter frequency and time-based organization with script specific arrangement design. This work represents the first optimized gaze-controlled Hindi virtual keyboard, which can be extended to other languages.

SacLab: A toolbox for saccade analysis to increase usability of eye tracking systems in clinical ophthalmology practice

PURPOSE

Many open source software packages have been recently developed to expand the usability of eye tracking systems to study oculomotor behavior, but none of these is specifically designed to encompass all the main functions required for creating eye tracking tests and for providing the automatic analysis of saccadic eye movements. The aim of this study is to introduce SacLab, an intuitive, freely-available MATLAB toolbox based on Graphical User Interfaces (GUIs) that we have developed to increase the usability of the ViewPoint EyeTracker (Arrington Research, Scottsdale, AZ, USA) in clinical ophthalmology practice.

METHODS

SacLab consists of four processing modules that enable the user to easily create visual stimuli tests (Test Designer), record saccadic eye movements (Data Recorder), analyze the recorded data to automatically extract saccadic parameters of clinical interest (Data Analyzer) and provide an aggregate analysis from multiple eye movements recordings (Saccade Analyzer), without requiring any programming effort by the user.

RESULTS

A demo application of SacLab to carry out eye tracking tests for the analysis of horizontal saccades was reported. We tested the usability of SacLab toolbox with three ophthalmologists who had no programming experience; the ophthalmologists were briefly trained in the use of SacLab GUIs and were asked to perform the demo application. The toolbox gained an enthusiastic feedback from all the clinicians in terms of intuitiveness, ease of use and flexibility. Test creation and data processing were accomplished in 52±21s and 46±19s, respectively, using the SacLab GUIs.

CONCLUSIONS

SacLab may represent a useful tool to ease the application of the ViewPoint EyeTracker system in clinical routine in ophthalmology.

Engagement Sensitive Visual Stimulation

Stroke is one of leading cause of death and disability worldwide. Early detection during golden hour and treatment of individual neurological dysfunction in stroke using easy-to-access biomarkers based on a simple-to-use, cost-effective, clinically-valid screening tool can bring a paradigm shift in healthcare, both urban and rural. In our research we have designed a quantitative automatic home-based oculomotor assessment tool that can play an important complementary role in prognosis of neurological disorders like stroke for the neurologist. Once the patient has been screened for stroke, the next step is to design proper rehabilitation platform to alleviate the disability. In addition to the screening platform, in our research, we work in designing virtual reality based rehabilitation exercise platform that has the potential to deliver visual stimulation and in turn contribute to improving one's performance.