Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online

• URL: http://arxiv.org/abs/2012.05373v1
• Date: Wed, 9 Dec 2020 23:53:32 GMT
• Title: Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online
• Authors: Mohammad Rafayet Ali, Taylor Myers, Ellen Wagner, Harshil Ratnu, E. Ray Dorsey, Ehsan Hoque
• Abstract summary: One of the symptoms of Parkinson's disease (PD) is hypomimia or reduced facial expressions. We analyzed the facial action units (AU) from videos of 604 individuals. Individuals with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals.
• Score: 0.6004833598578182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the symptoms of Parkinson's disease (PD) is hypomimia or reduced facial expressions. In this paper, we present a digital biomarker for PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, mean age 63.9 yo, sd 7.8 ) collected online using a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to those methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a biomarker for PD could be potentially transformative for patients in need of physical separation (e.g., due to COVID) or are immobile.

Related papers

• Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment [42.449532608247935]
  This paper presents a deep learning-based approach to automatically quantify turning angles by extracting 3D skeletons from videos. We utilise state-of-the-art human pose estimation models, Fastpose and Strided Transformer, on a total of 1386 turning video clips from 24 subjects. This is the first work to explore the use of single monocular camera data to quantify turns by PD patients in a home setting.
  arXiv  Detail & Related papers  (2024-08-15T14:36:07Z)
• OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition [66.91236298878383]
  Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns. We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
  arXiv  Detail & Related papers  (2024-02-29T01:20:29Z)
• PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition [1.9482539692051932]
  Parkinsons disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. We present PULSAR, a novel method to screen for PD from webcam-recorded videos of finger-tapping. We used an adaptive graph convolutional neural network to dynamically learn the temporal graph specific to the finger-tapping task.
  arXiv  Detail & Related papers  (2023-12-10T05:56:20Z)
• Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework [2.4914378616592505]
  This dataset includes 256 individuals with PD, 165 clinically diagnosed, and 91 self-reported. participants used webcams to record themselves mimicking three facial expressions. Facial landmarks are automatically tracked from the recordings to extract features related to hypomimia. Machine learning algorithms are trained on these features to distinguish between individuals with and without PD.
  arXiv  Detail & Related papers  (2023-08-03T18:23:37Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers [57.1091606948826]
  We propose a novel FER model, named Poker Face Vision Transformer or PF-ViT, to address these challenges. PF-ViT aims to separate and recognize the disturbance-agnostic emotion from a static facial image via generating its corresponding poker face. PF-ViT utilizes vanilla Vision Transformers, and its components are pre-trained as Masked Autoencoders on a large facial expression dataset.
  arXiv  Detail & Related papers  (2022-07-22T13:39:06Z)
• What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors [9.962268111440105]
  One of the methods that machines can recognize facial expression is analyzing the changes in face during facial expression presentation. Some of the most state-of-the-art classification algorithms such as C5.0, CRT, QUEST, CHAID, Deep Learning (DL), SVM and Discriminant algorithms were used to classify the extracted motion vectors. Experimental results on Extended Cohen-Kanade (CK+) facial expression dataset demonstrated that the best methods were DL, SVM and C5.0.
  arXiv  Detail & Related papers  (2021-09-12T08:17:44Z)
• I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition [78.07239208222599]
  We evaluate how the recently proposed FaceChannel adapts towards recognizing facial expressions from persons with masks. We also perform specific feature-level visualization to demonstrate how the inherent capabilities of the FaceChannel to learn and combine facial features change when in a constrained social interaction scenario.
  arXiv  Detail & Related papers  (2021-04-16T20:03:30Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• Exploring Facial Expressions and Affective Domains for Parkinson Detection [17.244432348845034]
  Parkinson's Disease (PD) is a neurological disorder that affects facial movements and non-verbal communication. We propose to use facial expression analysis from face images based on affective domains to improve PD detection.
  arXiv  Detail & Related papers  (2020-12-11T18:48:53Z)
• An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery [0.0]
  Parkinson's disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan. In this study, we propose a machine learning model that accurately classifies any given DaTscan as having Parkinson's disease or not.
  arXiv  Detail & Related papers  (2020-08-01T10:44:03Z)

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.