Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
- URL: http://arxiv.org/abs/2404.12984v2
- Date: Mon, 3 Jun 2024 10:45:42 GMT
- Title: Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
- Authors: Mateusz Daniol, Daria Hemmerling, Jakub Sikora, Pawel Jemiolo, Marek Wodzinski, Magdalena Wojcik-Pedziwiatr,
- Abstract summary: Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally.
This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements.
- Score: 0.2686968510141288
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative diseases. Clinical relevance - Developing a non-invasive biomarker for Parkinson's disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear.
Related papers
- AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review [0.09320657506524149]
Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early.
This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis.
arXiv Detail & Related papers (2024-10-21T12:26:53Z) - Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures [1.6000489723889526]
Ocular diseases, including diabetic retinopathy and glaucoma, present a significant public health challenge.
Deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging.
Challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making.
arXiv Detail & Related papers (2024-01-21T04:14:54Z) - Deep Learning Approaches for Seizure Video Analysis: A Review [40.1521024778093]
Computer-aided video analysis of seizures has emerged as a natural avenue.
Deep learning and computer vision approaches have driven substantial advancements.
Main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization.
arXiv Detail & Related papers (2023-12-18T04:45:41Z) - A store-and-forward cloud-based telemonitoring system for automatic
assessing dysarthria evolution in neurological diseases from video-recording
analysis [8.275082697744084]
Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech.
This work presents a store-and-forward telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria.
arXiv Detail & Related papers (2023-09-16T16:24:11Z) - Walk4Me: Telehealth Community Mobility Assessment, An Automated System
for Early Diagnosis and Disease Progression [2.96872688940436]
We introduce Walk4Me, a telehealth community mobility assessment system designed to facilitate early diagnosis, severity, and progression identification.
To accomplish this, we employ an Artificial Intelligence (AI)-based detection of gait characteristics in patients and typically developing peers.
Our system remotely and in real-time collects data from device sensors (e.g., acceleration from a mobile device, etc.) using our novel Walk4Me API.
arXiv Detail & Related papers (2023-05-05T10:47:34Z) - A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications.
These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
arXiv Detail & Related papers (2022-11-07T17:20:39Z) - A Deep Learning Approach for the Segmentation of Electroencephalography
Data in Eye Tracking Applications [56.458448869572294]
We introduce DETRtime, a novel framework for time-series segmentation of EEG data.
Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront.
Our model generalizes well in the task of EEG sleep stage segmentation.
arXiv Detail & Related papers (2022-06-17T10:17:24Z) - Unsupervised deep learning techniques for powdery mildew recognition
based on multispectral imaging [63.62764375279861]
This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves.
We focus on unsupervised deep learning techniques applied to multispectral imaging data.
We propose the use of autoencoder architectures to investigate two strategies for disease detection.
arXiv Detail & Related papers (2021-12-20T13:29:13Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD.
Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
arXiv Detail & Related papers (2020-08-21T20:22:55Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.