Application of blendshapes in tracking oromotor movements in healthy adults.
Purpose: Recent studies showed that face recognition technology can be applied in clinical diagnosis. Blendshapes are one of the technologies to track facial movement. Oromotor functions involve the coordination and movement of swallowing muscles. It is hypothesized that blendshapes can track select...
| Publicado en: | Health & Technology Vol. 16; no. 3; pp. 421 - 430 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193278012&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193278012 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21907188 BEWM jtl: Health & Technology issn: 21907188 maglogo: N pubinfo: dt: May2026 vid: 16 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193278012 191041856 193278012 193278012 10.1007/s12553-026-01053-2 193278012 ppf: 421 ppct: 9 formats: tig: atl: Application of blendshapes in tracking oromotor movements in healthy adults. aug: au: Liu, Joana Wong, Alan N. L. Chan, Karen Man-Kei affil: https://ror.org/02zhqgq86 Swallowing Research Laboratory, Faculty of Education, The University of Hong Kong, 7/F, Meng Wah Complex, Pokfulam, Hong Kong sug: subj: Task Performance and Analysis Mouth Physiology Speech Physiology Motion Capture Computer Graphics Facial Muscles Physiology Human Videorecording Female Male Cross Sectional Studies Adolescence Adult Middle Age Aged Aged, 80 and Over Hong Kong One-Way Analysis of Variance Descriptive Statistics Data Analysis Software Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: Purpose: Recent studies showed that face recognition technology can be applied in clinical diagnosis. Blendshapes are one of the technologies to track facial movement. Oromotor functions involve the coordination and movement of swallowing muscles. It is hypothesized that blendshapes can track selected oromotor movements and classify between normal and impaired oromotor functions. This study aims to identify the relevant blendshapes to differentiate between normal and impaired oromotor functions. Method: A total of 88 participants were recruited to carry out four oromotor tasks. All participants were instructed to conduct the tasks at their typical performance and simulate mildly and severely impaired oromotor functions. Movements were captured with an iPad Pro embedded with the TrueDepth camera. Two speech-language pathologists rated all videos independently on participants' performance. Result: Results showed that a small set of 5–13 parameters were significantly associated with each of the oromotor tasks, with large effect size differences across performance levels. Pairwise comparison revealed a significant difference in relevant parameters between normal and impaired oromotor functions. Conclusion: This study supports the use of automated face recognition technology to track oromotor movements in healthy adults and identified corresponding blendshapes. Further testing in individuals with oromotor impairment is recommended. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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