REVIEW article
Front. Neurosci.
Sec. Translational Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1646485
This article is part of the Research TopicApplications of Intelligent Sensing and Biomedical Information Processing in Clinical NeuroscienceView all 3 articles
A Review of the Application of Intelligent Sensing Technology in the Recognition and Evaluation of Facial Paralysis
Provisionally accepted
- 1Chengdu University of Traditional Chinese Medicine, Chengdu, China
- 2Sichuan Integrative Medicine Hospital, Chengdu, China
- 3Sichuan Institute for Translational Chinese Medicine, Chengdu, China
- 4Asia University, Taichung, Taiwan
- 5Sichuan Academy of Chinese Medicine Sciences, Chengdu, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Facial paralysis (FP), as a highly prevalent neurological dysfunction disease worldwide, has long faced challenges such as strong subjectivity in assessment and difficulty in quantifying therapeutic effects in its clinical diagnosis and treatment. Traditional scales rely on physicians' experience. Neuroelectrophysiological examinations are invasive, while imaging evaluations are costly. The rise of intelligent sensing technology provides a new path to break through these limitations. Intelligent sensing technology has significantly improved the accuracy of FP recognition and assessment through multi-modal data fusion and dynamic monitoring. Its clinical value is not only reflected in the improvement of diagnostic efficiency, but also in promoting a fundamental change in the diagnosis and treatment model of FP. The artificial intelligence-assisted analysis mainly focuses on using machine learning algorithms to conduct in-depth exploration and analysis of the surface electromyogram (sEMG) signals of patients with facial paralysis, the motion trajectory data obtained through three-dimensional (3D) motion capture, as well as the data from patients' self-assessment scales. This study systematically reviews the innovative applications of intelligent sensing technology in the recognition and evaluation of FP, focusing on three major technical directions: sEMG, 3D motion capture, and artificial intelligence assisted analysis.
Keywords: Intelligent sensing technology, Facial Paralysis, Surface electromyogram, Three-dimensional motion capture, artificial intelligence
Received: 13 Jun 2025; Accepted: 03 Sep 2025.
Copyright: © 2025 Tang, Zhang, Shao, Liu, Zhai, Xu, Lan, Yen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chao Wang, Sichuan Academy of Chinese Medicine Sciences, Chengdu, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.