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

• 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)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [59.85702949046042]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process. We verify that when our method is applied to healthy samples, the input images are reconstructed without significant modifications.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework [3.673889641081601]
  We collected 3,871 videos from 1,059 unique participants, including 256 self-reported PD patients. We extracted features relevant to Hypomimia, a prominent symptom of PD characterized by reduced facial expressions. An ensemble of AI models trained on these features achieved an accuracy of 89.7% and an Area Under the Receiver Operating Characteristic (AUROC) of 89.3%.
  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)
• Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system [63.20765930558542]
  We used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
  arXiv  Detail & Related papers  (2022-05-12T08:59:57Z)
• 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.