Document Type : Original Research

Authors

1 Department of Sport Biomechanics and Injuries, Faculty of Physical Education and Sports Sciences, Kharazmi University Tehran, Iran

2 Department of Biomechanics, Kinesiology Research Center, Kharazmi University Tehran, Iran

3 Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences Shiraz, Iran

4 Department of Orthotics and Prosthetics, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

10.31661/jbpe.v0i0.2210-1555

Abstract

Background: The development of a standard motion capture system is needed since the measurement of temporomandibular disorders is time-consuming and costly using laboratory tools.
Objective: The current study aimed to investigate the mandibular kinematic variables using a regular mobile phone and the motion analysis system.
Material and Methods: In this quasi-laboratory and comparative study, ten healthy individuals participated, and three mobile cameras, nine red markers, and Kinovea software were also used to investigate the mandibular kinematic variables. Nine light reflective markers were used to check the sensitivity, accuracy, and reliability of the proposed system. The motion was analyzed using seven motion analysis infrared cameras and Qualisys Track Manager (QTM) software. Two other raters analyzed the kinematic variables obtained from the mobile to measure intra- and inter-rater reliability.
Results: Pearson’s correlation coefficient was obtained at 0.98, 0.75, 0.98, and 0.96, showing a high correlation. The accuracy and reliability validation tests showed an average error and an accuracy of 0.156 mm and 95%, respectively, with a mobile phone. The Intra Class Correlation coefficient showed a high internal correlation in the mentioned confidence interval (0.98 and 0.81, and 0.96 and 0.97). The intraclass correlation coefficient method also showed 97% inter-raster reliability. 
Conclusion: Mobile phones as a new system can evaluate the kinematic variables of mandibular disorders with appropriate accuracy and reliability.

Highlights

Heydar Sadeghi (Google Scholar)

 

Keywords

  1. Cui H, Zhong W, Yang Z, Cao X, Dai S, Huang X, et al. Comparison of Facial Muscle Activation Patterns Between Healthy and Bell’s Palsy Subjects Using High-Density Surface Electromyography. Front Hum Neurosci. 2021;14:618985. doi: 10.3389/fnhum.2020.618985. PubMed PMID: 33510628. PubMed PMCID: PMC7835336.
  2. Calil BC, Da Cunha DV, Vieira MF, De Oliveira Andrade A, Furtado DA, Bellomo Junior DP, Pereira AA. Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features. Biomed Eng Online. 2020;19(1):22. doi: 10.1186/s12938-020-00764-5. PubMed PMID: 32295597. PubMed PMCID: PMC7161015.
  3. Azevedo TC, Tavares JM, Vaz MA. Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods. Comput Methods Biomech Biomed Engin. 2010;13(3):359-69. doi: 10.1080/10255840903251288. PubMed PMID: 19844816.
  4. Ahlers MO, Bernhardt O, Jakstat HA, Kordaß B, Türp JC, Schindler HJ, Hugger A. Motion analysis of the mandible: guidelines for standardized analysis of computer-assisted recording of condylar movements. Int J Comput Dent. 2015;18(3):201-23. PubMed PMID: 26389133.
  5. Baeyens JP, Gilomen H, Clijsen R. In vivo measurement of the 3d kinematics of the temporomandibular joint using minitiarized electromagnetic trackers. Isokinetics and Exercise Science. 2010;18:96-7.
  6. Jankelson B, Hoffman GM, Hendron Jr JA. The physiology of the stomatognathic system. J Am Dent Assoc. 1952;46(4):375-86. doi: 10.14219/jada.archive.1953.0070. PubMed PMID: 13034397.
  7. Kim DS, Choi SC, Lee SS, Heo MS, Huh KH, Hwang SJ, Yi WJ. Correlation between 3-dimensional facial morphology and mandibular movement during maximum mouth opening and closing. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2010;110(5):648-56. doi: 10.1016/j.tripleo.2010.06.007. PubMed PMID: 20955952.
  8. Mazzetto MO, Anacleto MA, Rodrigues CA, Bragança RM, Valencise Magri L. Comparison of mandibular movements in TMD by means of a 3D ultrasonic system and digital caliper rule. Cranio. 2017;35(1):46-51. doi: 10.1080/08869634.2016.1149928. PubMed PMID: 27077251.
  9. Enciso R, Fidaleo DA. 3D Jaw Modeling and Animation. In: Medicine Meets Virtual Reality 11: NextMed: Health Horizon. Netherlands: IOS Press; 2003. p. 65-8.
  10. Yuan F, Sui H, Li Z, Yang H, Lü P, Wang Y, Sun Y. A Method of Three-Dimensional Recording of Mandibular Movement Based on Two-Dimensional Image Feature Extraction. PLoS One. 2015;10(9):e0137507. doi: 10.1371/journal.pone.0137507. PubMed PMID: 26375800. PubMed PMCID: PMC4573517.
  11. Chen CC, Chen YJ, Chen SC, Lin HS, Lu TW. Evaluation of soft-tissue artifacts when using anatomical and technical markers to measure mandibular motion. Journal of Dental Sciences. 2011;6(2):95-101. doi: 10.1016/j.jds.2011.03.010.
  12. Furtado DA, Pereira AA, Andrade Ade O, Bellomo Jr DP, Da Silva MR. A specialized motion capture system for real-time analysis of mandibular movements using infrared cameras. Biomed Eng Online. 2013;12:17. doi: 10.1186/1475-925X-12-17. PubMed PMID: 23433470. PubMed PMCID: PMC3636046.
  13. Santos IC, Tavares JM, Mendes JG, Paulo MP. A system for analysis of the 3D mandibular movement using magnetic sensors and neuronal networks. In Proceedings of the 2nd International Workshop on Artificial Neural Networks and Intelligent Information Processing; United States: Scitepress - Science and Technology Publications; 2006. p. 54-63.
  14. Ziaiee M, Sadeghi H, Karimi MT. Evaluation of Mandibular Movements in Patients with Bell’s Palsy Using Kinematic Variables. Med J Islam Repub Iran. 2023;37:19. doi: 10.47176/mjiri.37.19. PubMed PMID: 37123332. PubMed PMCID: PMC10134089.
  15. Zhao T, Yang H, Sui H, Salvi SS, Wang Y, Sun Y. Accuracy of a Real-Time, Computerized, Binocular, Three-Dimensional Trajectory-Tracking Device for Recording Functional Mandibular Movements. PLoS One. 2016;11(10):e0163934. doi: 10.1371/journal.pone.0163934. PubMed PMID: 27701462. PubMed PMCID: PMC5049779.
  16. Fang JJ, Kuo TH. Modelling of mandibular movement. Comput Biol Med. 2008;38(11-12):1152-62. doi: 10.1016/j.compbiomed.2008.09.001. PubMed PMID: 18976989.
  17. Mostashiri N, Dhupia JS, Verl AW, Xu W. A Novel Spatial Mandibular Motion-Capture System Based on Planar Fiducial Markers. IEEE Sensors Journal. 2018;18(24):10096-104. doi: 10.1109/JSEN.2018.2873349.
  18. Carossa M, Cavagnetto D, Ceruti P, Mussano F, Carossa S. Individual mandibular movement registration and reproduction using an optoeletronic jaw movement analyzer and a dedicated robot: a dental technique. BMC Oral Health. 2020;20(1):271. doi: 10.1186/s12903-020-01257-6. PubMed PMID: 33028288. PubMed PMCID: PMC7542888.
  19. Moosaei M, Pourebadi M, Riek LD. Modeling and synthesizing idiopathic facial paralysis. In 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019); Lille, France: IEEE; 2019. p. 1-8.
  20. Chen CC, Lin CC, Chen YJ, Hong SW, Lu TW. A method for measuring three-dimensional mandibular kinematics in vivo using single-plane fluoroscopy. Dentomaxillofac Radiol. 2013;42(1):95958184. doi: 10.1259/dmfr/95958184. PubMed PMID: 22842637. PubMed PMCID: PMC5083121.
  21. Tanaka Y, Yamada T, Maeda Y, Ikebe K. Markerless three-dimensional tracking of masticatory movement. J Biomech. 2016;49(3):442-9. doi: 10.1016/j.jbiomech.2016.01.011. PubMed PMID: 26827172.
  22. Tian SK, Dai N, Li LL, Li WW, Sun YC, Cheng XS. Three-dimensional mandibular motion trajectory-tracking system based on BP neural network. Math Biosci Eng. 2020;17(5):5709-26. doi: 10.3934/mbe.2020307. PubMed PMID: 33120574.
  23. Leissner O, Maulén-Yáñez M, Meeder-Bella W, León-Morales C, Vergara-Bruna E, González-Arriagada WA. Assessment of mandibular kinematics values and its relevance for the diagnosis of temporomandibular joint disorders. J Dent Sci. 2021;16(1):241-8. doi: 10.1016/j.jds.2020.05.015. PubMed PMID: 33384804. PubMed PMCID: PMC7770294.
  24. Sassi FC, Mangilli LD, Poluca MC, Bento RF, Andrade CR. Mandibular range of motion in patients with idiopathic peripheral facial palsy. Braz J Otorhinolaryngol. 2011;77(2):237-44. doi: 10.1590/s1808-86942011000200014. PubMed PMID: 21537626. PubMed PMCID: PMC9450800.
  25. Ingawalé S, Goswami T. Temporomandibular joint: disorders, treatments, and biomechanics. Ann Biomed Eng. 2009;37(5):976-96. doi: 10.1007/s10439-009-9659-4. PubMed PMID: 19252985.
  26. Toledo PN. Effect of myofunctional therapy on patients with long-term facial paralysis associate to the application of botulinum toxin tion [dissertation]. University of Sao Paulo; 2007.
  27. Brackmann DE, Fisher LM, Hansen M, Halim A, Slattery WH. The effect of famciclovir on delayed facial paralysis after acoustic tumor resection. 2008;118(9):1617-20. doi: 10.1097/MLG.0b013e3181788d5d. PubMed PMID: 18596563.
  28. Coulson SE, Croxson GR, Adams RD, O’Dwyer NJ. Reliability of the “Sydney,” “Sunnybrook,” and “House Brackmann” facial grading systems to assess voluntary movement and synkinesis after facial nerve paralysis. Otolaryngol Head Neck Surg. 2005;132(4):543-9. doi: 10.1016/j.otohns.2005.01.027. PubMed PMID: 15806042.
  29. Kanerva M, Poussa T, Pitkäranta A. Sunnybrook and House-Brackmann Facial Grading Systems: intrarater repeatability and interrater agreement. Otolaryngol Head Neck Surg. 2006;135(6):865-71. doi: 10.1016/j.otohns.2006.05.748. PubMed PMID: 17141075.
  30. Baeyens JP, Gilomen H, Erdmann B, Clijsen R, Cabri J, Vissers D. In vivo measurement of the 3D kinematics of the temporomandibular joint using miniaturized electromagnetic trackers: technical report. Med Biol Eng Comput. 2013;51(4):479-84. doi: 10.1007/s11517-012-1015-4. PubMed PMID: 23242785.