ArticleJournal of speech, language, and hearing research : JSLHR2022
Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation.
Article in Journal of speech, language, and hearing research : JSLHR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Speech-based digital endpoints track ALS progression and align with standard clinical outcomes: evidence from the VRG50635 trial.Scientific reports · 2026Article
- Automated video analysis for early detection of bradykinesia in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Video analysis reveals early signs of Bradykinesia in REM sleep behavior disorder and Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Discriminating Children with Speech Sound Disorders from Children with Typically Developing Speech Using the Motor Speech Hierarchy Probe Words: A Preliminary Analysis of Mandibular Control.Diagnostics (Basel, Switzerland) · 2025Article
- [Research progress in motor assessment of neurodegenerative diseases driven by motion capture data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Facial Movements Extracted from Video for the Kinematic Classification of Speech.Sensors (Basel, Switzerland) · 2024Article
- Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024Review
- What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.Parkinsonism & related disorders · 2024Article
- Multimodal speech biomarkers for remote monitoring of ALS disease progression.Computers in biology and medicine · 2024Article
- Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression.medRxiv : the preprint server for health sciences · 2024Article
- A multimodal approach to automated hierarchical assessment of bulbar involvement in amyotrophic lateral sclerosis.Frontiers in neurology · 2024Article
- Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation.IEEE journal of translational engineering in health and medicine · 2024Article
- Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Analytical Validation of a Webcam-Based Assessment of Speech Kinematics: Digital Biomarker Evaluation following the V3 Framework.Digital biomarkersArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeFacial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade hardware and machine/deep learning made it possible to estimate facial movements from videos. This study aimed to establish the clinical validity of a video-based facial analysis for disease staging classification and estimation of clinical scores.
methodFifteen individuals with ALS and 11 controls participated in this study. Participants with ALS were stratified into early and late bulbar ALS groups based on their speaking rate. Participants were recorded with a three-dimensional (3D) camera (color + depth) while repeating a simple sentence 10 times. The lips and jaw movements were estimated, and features related to sentence duration and facial movements were used to train a machine learning model for multiclass classification and to predict the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore and speaking rate.
resultsThe classification model successfully separated healthy controls, the early ALS group, and the late ALS group with an overall accuracy of 96.1%. Video-based features demonstrated a high ability to estimate the speaking rate (adjusted
conclusionsThe proposed approach based on a 3D camera and machine learning algorithms represents an easy-to-use and inexpensive system that can be included as part of a clinical assessment of bulbar ALS to integrate facial movement analysis with other clinical data seamlessly.
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