3D Visualization of Gait Analysis Using Unity Program

Yazarlar

DOI:

https://doi.org/10.5281/zenodo.10813098

Anahtar Kelimeler:

3D visualization, Gait analysis, Sensors, Signal filtering, Unity

Özet

This article includes a study on 3D visualization of gait analysis using Unity program to determine the reason for gait disorders. Here, the movements of the right tibia and the force generated in determined areas of the sole of the foot for the 20-second gait of a person were monitored with 3D visualized data. MPU-6050 module, which includes gyroscope and acceleration sensors to detect right foot movements, was used to perform gait analyses. We took force data from six areas determined on the sole by FSR sensors. These sensor data were transferred to the computer using wireless communication with the help of NodeMCU Wi-Fi modules working as server and client. After the sensors' data were normalized, we compared the signals of these data using different filtering methods. As a result of these comparisons, we determined that the low-pass filtering method with an optimized cut-off frequency of 5 Hz was more suitable than the other filtering methods. For this reason, we use low-pass filtering on the signals obtained for 3D data visualization. After filtering the signals of angle, acceleration, and force data received in real-time, we animatedly visualize the data of these signals with 3D column chart and heatmap visualization techniques. In our study, the 3D visualization method used for gait analysis can contribute to diagnosing and treating foot disorders that cause gait disorders.

Referanslar

Altural, H., Özkan, S., & Yılmaz, G. (2019). Serebral Palsili Çocuklar için Giyilebilir Robotik Sistem Tasarımı ve Simülasyonu. International Journal of Multidisciplinary Studies and Innovative Technologies, 3(2), 99-104.

Bamberg, S. J. M., Benbasat, A. Y., Scarborough, D. M., Krebs, D. E., & Paradiso, J. A. (2008). Gait analysis using a shoe-integrated wireless sensor system. IEEE transactions on information technology in biomedicine, 12(4), 413-423.

Boucharas, D., Androutsos, C., Gkois, G., Tsakanikas, V., Pezoulas, V., Manousos, D., ... & Fotiadis, D. (2022). Smart insole: A gait analysis monitoring platform targeting Parkinson disease patients based on insoles. arXiv preprint arXiv:2212.00109.

Chen, Y. J., Wu, C. M., Chen, P. C., See, A. R., & Chen, S. C. (2022). Pressure-sensor-based Gait Analysis for Disabled People. Sensors & Materials, 34.

Du, C., Graham, S., Depp, C., & Nguyen, T. (2021). Multi-task center-of-pressure metrics estimation with graph convolutional network. IEEE Transactions on Multimedia, 24, 2018-2033.

Dyulicheva, Y. Y., Gaponov, D. A., Mladenovic, R., & Kosova, Y. A. (2021, February). The virtual reality simulator development for dental students training: a pilot study. In AREdu (pp. 56-67).

Fu, Z., Hong, S., Zhang, R., & Du, S. (2021). Artificial-intelligence-enhanced mobile system for cardiovascular health management. Sensors, 21(3), 773.

Khaksar, S., Pan, H., Borazjani, B., Murray, I., Agrawal, H., Liu, W., ... & Walmsley, C. (2021). Application of inertial measurement units and machine learning classification in cerebral palsy: Randomized controlled trial. JMIR Rehabilitation and Assistive Technologies, 8(4), e29769.

Kim, H., Kang, Y., Valencia, D. R., & Kim, D. (2018, January). An integrated system for gait analysis using FSRs and an IMU. In 2018 Second IEEE International Conference on Robotic Computing (IRC) (pp. 347-351). IEEE.

Kristianslund, E., Krosshaug, T., & Van den Bogert, A. J. (2012). Effect of low pass filtering on joint moments from inverse dynamics: implications for injury prevention. Journal of biomechanics, 45(4), 666-671.

Lee, S. H., Kim, Y. S., & Yeo, W. H. (2021). Advances in microsensors and wearable bioelectronics for digital stethoscopes in health monitoring and disease diagnosis. Advanced Healthcare Materials, 10(22), 2101400.

Martínez-Martí, F., Martínez-García, M. S., García-Díaz, S. G., García-Jiménez, J., Palma, A. J., & Carvajal, M. A. (2014). Embedded sensor insole for wireless measurement of gait parameters. Australasian physical & engineering sciences in medicine, 37, 25-35.

Moro, C., Štromberga, Z., Raikos, A., & Stirling, A. (2017). The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anatomical sciences education, 10(6), 549-559.

Mustafaoglu, A., & Aktaş, F. (2022). IoMT-Based Smart Shoe Design for Flat Feet and Healthy Foot Gait Analysis. Avrupa Bilim ve Teknoloji Dergisi, (42), 108-112.

Özkan, S., Altural, H., Kandemirli, G. Ç., & Kandemirli, F. (2018, November). Orthopedic Insole Design and Pressure Analyzes. In Proceedings of Medical Technologies Congress (TIPTEKNO’18) (pp. 15-18), Gazi Magosa, KKTC.

Patil, J., Nandur, D., Mellikeri, M., Naik, K., & Kulkarni, P. (2016, March). Integrated sensor system for gait analysis. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 2298-2301). IEEE.

Pires, F., Costa, C., & Dias, P. (2021). On the use of virtual reality for medical imaging visualization. Journal of Digital Imaging, 34, 1034-1048.

Rotoni, G. M., Unabia, S. A., & Villaverde, J. F. (2020, September). Wireless Accelerometer-based Motion Recognition Sensors for Limb Movement Analysis in Babies. In Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology (pp. 311-315).

Sony, M. D., Isaac, J. S., Ezhilarasi, N., & Jayanth, V. (2021, June). IOT Based Infant Healthcare Monitoring System. In Journal of Physics: Conference Series (Vol. 1937, No. 1, p. 012050). IOP Publishing.

Tresser, S., Kuflik, T., Levin, I., & Weiss, P. L. (2021). Personalized rehabilitation for children with cerebral palsy. User modeling and user-adapted interaction, 31(4), 829-865.

Vullings, R., De Vries, B., & Bergmans, J. W. (2010). An adaptive Kalman filter for ECG signal enhancement. IEEE transactions on biomedical engineering, 58(4), 1094-1103.

Zheng, C. Y., & Yunus, J. (2014). Wearable Movement Analysis System for Children with Movement Disorders-Lower Extremities Assessment System. In The 15th International Conference on Biomedical Engineering: ICBME 2013, 4th to 7th December 2013, Singapore (pp. 395-398). Springer International Publishing.

Żuk, M., Wojtków, M., Popek, M., Mazur, J., & Bulińska, K. (2022). Three-dimensional gait analysis using a virtual reality tracking system. Measurement, 188, 110627.

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Yayınlanmış

2024-03-15

Nasıl Atıf Yapılır

ALTURAL, H., & ÖZKAN, S. (2024). 3D Visualization of Gait Analysis Using Unity Program. Journal on Mathematic, Engineering and Natural Sciences (EJONS), 8(1), 30–40. https://doi.org/10.5281/zenodo.10813098

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