Application of blendshapes in tracking oromotor movements in healthy adults

Adjabi I, Ouahabi A, Benzaoui A, Taleb-Ahmed A. Past, present, and future of face recognition: a review. Electronics. 2020;9:1188. https://doi.org/10.3390/electronics9081188.

Article  Google Scholar 

Hassaballah M, Aly S. Face recognition: challenges, achievements and future directions. IET Comput Vis. 2015;9(4):614–26. https://doi.org/10.1049/iet-cvi.2014.0084.

Article  Google Scholar 

Esselink L, Oomen M, Roelofsen F. Truedepth measurements of facial expressions: Sensitivity to the angle between camera and face. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW); 2023. p. 1–5. https://doi.org/10.1109/ICASSPW59220.2023.10193107

Vogt M, Rips A, Emmelmann C. Comparison of iPad Pro®’s LiDAR and TrueDepth capabilities with an industrial 3D scanning solution. Technologies. 2021;9(2):25. https://doi.org/10.3390/technologies9020025.

Article  Google Scholar 

Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H. Review on facial-recognition-based applications in disease diagnosis. Bioengineering. 2022;9(7):273. https://doi.org/10.3390/bioengineering9070273.

Article  Google Scholar 

Miller RE, Learned-Miller EG, Trainer P, Paisley A, Blanz V. Early diagnosis of acromegaly: computers vs clinicians. Clin Endocrinol. 2011;75(2):226–31. https://doi.org/10.1111/j.1365-2265.2011.04020.x.

Article  Google Scholar 

Pan Z, Shen Z, Zhu H, Bao Y, Liang S, Wang S, Li X, Niu L, Dong X, Shang X, Chen S, Pan H, Xiong G. Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome. Endocrine. 2021;72(3):865–73. https://doi.org/10.1007/s12020-020-02539-3.

Article  Google Scholar 

Geremek M, Szklanny K. Deep learning-based analysis of face images as a screening tool for genetic syndromes. Sensors. 2021. https://doi.org/10.3390/s21196595.

Article  Google Scholar 

Schneider HJ, Kosilek RP, Günther M, Roemmler J, Stalla GK, Sievers C, et al. A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification. J Clin Endocrinol Metab. 2011;96(7):2074–80. https://doi.org/10.1210/jc.2011-0237.

Article  Google Scholar 

Chen S, Pan Z-X, Zhu H-J, Wang Q, Yang J-J, Lei Y, et al. Development of a computer-aided tool for the pattern recognition of facial features in diagnosing Turner syndrome: comparison of diagnostic accuracy with clinical workers. Sci Rep. 2018;8(1):9317. https://doi.org/10.1038/s41598-018-27586-9.

Article  Google Scholar 

Scherr SA, Kammler C, Elberzhager F. Detecting user emotions with the true-depth camera to support mobile app quality assurance. 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (Seaa). IEEE; 2019.

Hartmann TJ, Hartmann JB, Friebe-Hoffmann U, Lato C, Janni W, Lato K. Novel method for three-dimensional facial expression recognition using self-normalizing neural networks and mobile devices. Geburtshilfe Frauenheilkd. 2022;82(9):955–69. https://doi.org/10.1055/a-1866-2943.

Article  Google Scholar 

Menzel T, Botsch M, Latoschik ME. Automated blendshape personalization for faithful face animations using commodity smartphones. Proceedings of the 28th ACM Symposium on Virtual Reality Software and Technology; 2022. p. 1–9. https://doi.org/10.1145/3562939.3565622.

Onizuka H, Thomas D, Uchiyama H, Taniguchi R. Landmark-guided deformation transfer of template facial expressions for automatic generation of avatar blendshapes. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops; 2019. p. 0–0.

Thomas D, Taniguchi R. Augmented blendshapes for real-time simultaneous 3D head modeling and facial motion capture. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 3299–3308.

Chen J, Ma X, Wang L, Cheng J. Blendshape-based migratable speech-driven 3D facial animation with overlapping chunking-transformer. Pattern Recognit Comput Vis. 2024;41–53. https://doi.org/10.1007/978-981-99-8432-9_4.

Lewis JP, Anjyo K, Rhee T, Zhang M, Pighin FH, Deng Z. Practice and theory of blendshape facial models. Eurographics (State Art Reports). 2014;1(8):1–23. https://doi.org/10.2312/egst.20141042.

Article  Google Scholar 

Somepalli MR, Charan MS, Shruthi S, Palaniswamy S. Implementation of single camera markerless facial motion capture using blendshapes. 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India; 2021.

Carrigan E, Zell E, Guiard C, McDonnell R. Expression packing: as-few‐as‐possible training expressions for blendshape transfer. Comput Graph Forum. 2020;39(2):219–33. https://doi.org/10.1111/cgf.13925.

Article  Google Scholar 

Han JH, Kim JI, Suh JW, Kim H. Customizing blendshapes to capture facial details. J Supercomput. 2023;79(6):6347–72. https://doi.org/10.1007/s11227-022-04885-7.

Article  Google Scholar 

Ingale AK, Leema AA, Kim H, Udayan JD. Automatic 3D facial landmark-based deformation transfer on facial variants for blendshape generation. Arab J Sci Eng. 2023;48(8):10109–23. https://doi.org/10.1007/s13369-022-07403-2.

Article  Google Scholar 

Matsuo K, Palmer JB. Anatomy and physiology of feeding and swallowing: normal and abnormal. Phys Med Rehabil Clin N Am. 2008;19(4):691–707. https://doi.org/10.1016/j.pmr.2008.06.001.

Article  Google Scholar 

Murry T, Chan K, Walsh EH. Clinical management of swallowing disorders. 6th ed. Plural Publishing; 2024.

Ambrocio KR, Miles A, Bhutada AM, Choi D, Garand KL. Defining normal sequential swallowing biomechanics. Dysphagia. 2023;38(6):1497–510. https://doi.org/10.1007/s00455-023-10576-z.

Article  Google Scholar 

Rech R, Baumgarten A, Colvara B, Brochier C, de Goulart B, Hugo F, et al. Association between oropharyngeal dysphagia, oral functionality, and oral sensorimotor alteration. Oral Dis. 2018;24(4):664–72. https://doi.org/10.1111/odi.12809.

Article  Google Scholar 

Nishiwaki K, Tsuji T, Liu MG, Hase K, Tanaka N, Fujiwara T. Identification of a simple screening tool for dysphagia in patients with stroke using factor analysis of multiple dysphagia variables. J Rehabil Med. 2005;37(4):247–51. https://doi.org/10.1080/16501970510026999.

Article  Google Scholar 

Wu Y, Guo K, Chu Y, Wang Z, Yang H, Zhang J. Advancements and challenges in non-invasive sensor technologies for swallowing assessment: a review. Bioengineering. 2024;11(5):430. https://doi.org/10.3390/bioengineering11050430.

Article  Google Scholar 

Wieseke A, Bantz D, Siktberg L, Dillard N. Assessment and early diagnosis of dysphagia. Geriatr Nurs. 2008;29(6):376–83. https://doi.org/10.1016/j.gerinurse.2007.12.001.

Article  Google Scholar 

Huang Y-F, Chang W-H, Liao Y-F, Chen M-H, Chang C-T. Lip and tongue strength associated with chewing patterns in aging population. BMC Oral Health. 2023;23(1):1–5. https://doi.org/10.1186/s12903-023-03503-z.

Article  Google Scholar 

Jeong D-M, Shin Y-J, Lee N-R, Lim H-K, Choung H-W, Pang K-M, et al. Maximal strength and endurance scores of the tongue, lip, and cheek in healthy, normal Koreans. J Korean Assoc Oral Maxillofac Surg. 2017;43(4):221–8. https://doi.org/10.5125/jkaoms.2017.43.4.221.

Article  Google Scholar 

Alagiakrishnan K, Bhanji RA, Kurian M. Evaluation and management of oropharyngeal dysphagia in different types of dementia: a systematic review. Arch Gerontol Geriatr. 2013;56(1):1–9. https://doi.org/10.1016/j.archger.2012.04.011.

Article  Google Scholar 

Audag N, Goubau C, Toussaint M, Reychler G. Screening and evaluation tools of dysphagia in adults with neuromuscular diseases: a systematic review. Ther Adv Chronic Dis. 2019;10:2040622318821622. https://doi.org/10.1177/2040622318821622.

Article  Google Scholar 

Magalhães Junior HV, Pernambuco LdeA, Lima KC, Ferreira MAF. Screening for oropharyngeal dysphagia in older adults: a systematic review of self-reported questionnaires. Gerodontology. 2018;35(3):162–9. https://doi.org/10.1111/ger.12333.

Article  Google Scholar 

Daniels SK, Anderson JA, Willson PC. Valid items for screening dysphagia risk in patients with stroke. Stroke. 2012;43(3):892–7. https://doi.org/10.1161/STROKEAHA.111.640946.

Article  Google Scholar 

Adams V, Mathisen B, Baines S, Lazarus C, Callister R. A systematic review and meta-analysis of measurements of tongue and hand strength and endurance using the Iowa oral performance instrument (IOPI). Dysphagia. 2013;28(3):350–69. https://doi.org/10.1007/s00455-013-9451-3.

Article  Google Scholar 

Clark HM, Solomon NP. Age and sex differences in orofacial strength. Dysphagia. 2012;27(1):2–9. https://doi.org/10.1007/s00455-011-9328-2.

Article  Google Scholar 

Yoshikawa M, Fukuoka T, Mori T, Hiraoka A, Higa C, Kuroki A, Takeda C, Maruyama M, Yoshida M, Tsuga K. Comparison of the Iowa oral performance instrument and JMS tongue pressure measurement device. J Dent Sci. 2021;16(1):214–9. https://doi.org/10.1016/j.jds.2020.06.005.

Article  Google Scholar 

Sjögreen L, Lohmander A, KILIARIDIS S. Exploring quantitative methods for evaluation of lip function. J Rehabil Med. 2011;38(6):410–22. https://doi.org/10.1111/j.1365-2842.2010.02168.x.

Article  Google Scholar 

Truong C, Oudre L, Vayatis N. Selective review of offline change point detection methods. Signal Process. 2020;167:107299. https://doi.org/10.1016/j.sigpro.2019.107299.

Article  Google Scholar 

Chua DMN, Yuen-Yu C, Chan KM-K. Effects of oropharyngeal exercises on the swallowing mechanism of older adults: a systematic review. Int J Speech Lang Pathol. 2024;26(5):696–713. https://doi.org/10.1080/17549507.2023.2221409.

Article  Google Scholar 

Giraldo-Cadavid LF, Insignares D, Velasco V, Londoño N, Galvis AM, Rengifo ML, Bastidas-Goyes AR. Fiberoptic endoscopy evaluation of swallowing (FEES) findings associated with high pneumonia risk in a cohort of patients at risk of dysphagia. Dysphagia. 2024. https://doi.org/10.1007/s00455-024-10727-w.

Article  Google Scholar 

Giraldo-Cadavid LF, Leal-Leaño LR, Leon-Basantes GA, Bastidas AR, Garcia R, Ovalle S, et al. Accuracy of endoscopic and videofluoroscopic evaluations of swallowing for oropharyngeal dysphagia. Laryngoscope. 2017;127(9):2002–10. https://doi.org/10.1002/lary.26419.

Article  Google Scholar 

Helliwell K, Hughes VJ, Bennion CM, Manning-Stanley A. The use of videofluoroscopy (VFS) and fibreoptic endoscopic evaluation of swallowing (FEES) in the investigation of oropharyngeal dysphagia in stroke patients: a narrative review. Radiography. 2023;29(2):284–90. https://doi.org/10.1016/j.radi.2022.12.007.

Article  Google Scholar 

So BP-H, Chan TT-C, Liu L, Yip CC-K, Lim H-J, Lam W-K, et al. Swallow detection with acoustics and accelerometric-based wearable technology: a scoping review. Int J Environ Res Public Health. 2023;20(1):170.

Comments (0)

No login
gif