Abstract
This paper introduces an augmented reality (AR) visualisation based on the three-dimensional palpation code (3DP code) to enhance palpation training in cardiovascular examination. Traditional palpation training methods, such as textbook descriptions and subjective evaluations, may fail to adequately differentiate between tactile image patterns under varying vascular conditions. Moreover, the large amount of data involved in creating dynamic 3D tactile images for palpation complicates their storage, transmission, and display. Our method, demonstrated using a typical artery palpation example, provides interactive 3D visualisations and interactions, accompanied by efficient encoding, decoding, and compression techniques for large tactile data sets. Assessment results show a data compression ratio of 1/360, preserving over 95% of physiological information. The proposed webAR program is also highly adaptable, performing smoothly on most mobile platforms, providing potential benefits for medical and academic communities in improving teaching experience.
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References
Al-Zoube, A.M.: Efficient vision-based multi-target augmented reality in the browser. Multimed. Tools Appl. 81, 14303–14320 (2022)
Ashwell, M., Gunn, P., Gibson, S.: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Rev. 13(3), 275–286 (2012)
Baker, L., Ventura, J., Langlotz, T., et al.: Localization and tracking of stationary users for augmented reality. Vis. Comput. 2, 1–18 (2023)
Ball, J.W., Dains, J.E., Flynn, J.A.: Seidel’s guide to physical examination: an interprofessional approach. Elsevier Health Sciences, St. Louis, Missouri (2014)
Bussieck, M.R., Drud, A.S., Meeraus, A.: MINLPLib–a collection of test models for mixed-integer nonlinear programming. INFORMS J. Comput. 15(1), 114–119 (2003)
Chen, Y., Zhang, L., Zhang, D., Zhang, D.: Wrist pulse signal diagnosis using modified gaussian models and fuzzy C-Means classification. Med. Eng. Phys. 31(10), 1283–1289 (2009)
Chen, Y., Zhang, L., Zhang, D., Zhang, D.: Computerized wrist pulse signal diagnosis using modified auto-regressive models. J. Med. Syst. 35(3), 321–328 (2011)
Ertugrul, E., Zhang, H., Zhu, F., Lu, P., Li, P., Sheng, B., et al.: Embedding 3D models in offline physical environments. Comput. Animat. Virtual Worlds 31(4–5), 1–15 (2020)
Gauthier, C.J., Lefort, M., Mekary, S., Desjardins-Crépeau, L., Skimminge, A., Iversen, P., et al.: Hearts and minds: linking vascular rigidity and aerobic fitness with cognitive aging. Neurobiol. Aging 36(1), 304–314 (2015)
Hanna, M.G., Ahmed, I., Nine, J., Prajapati, S., Pantanowitz, L.: Augmented reality technology using Microsoft HoloLens in anatomic pathology. Arch. Pathol. Lab. Med. 142(5), 638–644 (2018)
Hussain, R., Lalande, A., Guigou, C., Bozorg-Grayeli, A.: Contribution of augmented reality to minimally invasive computer-assisted cranial base surgery. IEEE J. Biomed. Health Inform. 24(7), 2093–2106 (2020)
Jeon, Y.J., Kim, J.U., Lee, H.J., Lee, J., Ryu, H.H., Lee, Y.J., et al.: A clinical study of the pulse wave characteristics at the three pulse diagnosis positions of Chon, Gwan and Cheok. Evid. Based. Complement. Alternat. Med. 2011, 904056 (2011)
Lee, B.J., Jeon, Y.J., Bae, J.H., Yim, M.H., Kim, J.Y.: Gender differences in arterial pulse wave and anatomical properties in healthy Korean adults. Eur. J. Integr. Med. 25, 41–48 (2019)
Luo, C.H., Su, C.J., Huang, T.Y., Chung, C.Y.: Non-invasive holistic health measurements using pulse diagnosis: i. validation by three-dimensional pulse mapping. Eur. J. Integr. Med. 8(6), 921–925 (2016)
Luo, C.H., Zhang, Z., Peng, B., Xie, X., Lee, T.L., Tsai, L.M.: The novel three-dimensional pulse images analyzed by dynamic l-cube polynomial model. Med. Biol. Eng. Comput. 59(2), 315–326 (2021)
McGee, S.: Evidence-based Physical Diagnosis, 5th edn. Elsevier - Health Sciences Division, Philadelphia, PA (2021)
Peng, B., et al.: Cross-channel dynamic weighting RPCA: a de-noising algorithm for multi-channel arterial pulse signal. Appl. Sci. 2022(6), 2931 (2022)
Peng, B., Luo, C.H., Chan, W.Y., Shieh, M.D., Su, C.J., Tai, C.C.: Development and testing of a prototype for 3D radial pulse image measurement and compatible with 1D pulse wave analysis. IEEE Access 7, 182846–182859 (2019)
Peng, B., Luo, C.H., Sinha, N., Tai, C.C., Xie, X., Xie, H.: Fourier series analysis for novel spatiotemporal pulse waves: normal, taut, and slippery pulse images. Evid. Based Complement. Altern. Med. 2019, 5734018 (2019)
Potenziani, M., Callieri, M., Dellepiane, M., Corsini, M., Ponchio, F., Scopigno, R.: 3DHOP: 3d heritage online presenter. Comput. Graph. 52, 129–141 (2015)
Roy, S.G., Kanjilal, U.: Web-based augmented reality for information delivery services: a performance study. DESIDOC J. Libr. Inf. Technol. 41(3), 167–174 (2021)
Sandwell, D.T.: Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data. Geophys. Res. Lett. 14(2), 139–142 (1987)
Shi, Y., Yu, C.: Intelligent interaction in mixed reality. Virtual Real. Intell. Hardw. 4(2), ii–iii (2022)
Upadhyay, K., Agrawal, M., Vashist, P.: Learning multi-scale deep fusion for retinal blood vessel extraction in fundus images. Vis. Comput. 7, 1–13 (2022)
Vlachopoulos, C., O’rourke, M., Nichols, W.W.: McDonald’s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles. CRC Press, London (2011)
Wang, D., Zhang, D., Lu, G.: Generalized feature extraction for wrist pulse analysis: from 1D time series to 2D matrix. IEEE J. Biomed. Health Inform. 21(4), 978–985 (2017)
Wang, L., Xu, L., Feng, S., Meng, M.Q.H., Kuanquan, W.: Multi-Gaussian fitting for pulse waveform using weighted least squares and multi-criteria decision making method. Comput. Biol. Med. 43(11), 1661–1672 (2013)
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This research was funded by the National Natural Science Foundation of China, grant number 62071497.
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Chen, Z., Peng, B., Gong, K., Hao, Y., Xie, X. (2024). 3DP Code-Based Compression and AR Visualization for Cardiovascular Palpation Training. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_37
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