STUDY OF METHODS FOR DETECTING OPTICAL MARKERS IN THE SYSTEM OF HUMAN GAIT AND POSTURE ANALYSIS
Article Sidebar
Main Article Content
Abstract
The study is dedicated to the relevant topic of automated detection of muscle imbalance and postural deformities, which is particularly in demand among patients with orthopedic prostheses and in pediatric orthopedics. The authors propose a portable monitoring system that uses computer vision methods to assess the level of the pelvis, shoulders, and shoulder blades, ensuring the storage of photogrammetric data for subsequent analysis of rehabilitation results. The purpose of the work is to study methods for detecting optical markers on the human body when analyzing gait. The research tasks included conducting an analysis with a justification of the need to study computer graphics methods in the context of photogrammetric systems used in rehabilitation orthopedics; studying the impact of color characteristics of markers on detection accuracy; studying the impact of marker shape on detection accuracy; and analyzing the obtained results. The subject of the study is computer graphics and machine vision methods for detecting markers on the subject's body. The object of the study is photogrammetric technologies in orthopedics. As a result of the study, it was established that the use of the HSV color format for marker detection demonstrates high accuracy and low error even under changing lighting conditions. It was found that the shape of the marker affects detection accuracy, with the best results shown by the square shape. The research results confirmed the feasibility of using photogrammetry methods to assess joint asymmetry and muscle imbalance. Further research will focus on increasing the speed and accuracy of marker detection with non-stationary camera placement and a complicated background.
Article Details
References
Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M. and Khan, M. K. (2018), “Medical Image Analysis using Convolutional Neural Networks: A Review”, J Med Syst, vol. 42, art. no. 226, https://doi.org/10.1007/s10916-018-1088-1
Shen, D., Wu, G. and Suk, H.I. (2017), “Deep Learning in Medical Image Analysis”, Annu Rev Biomed Eng, vol. 19, pp. 221–248, doi: https://doi.org/10.1146/annurev-bioeng-071516-044442
Barrios-Muriel, J., Romero-Sánchez, F., Alonso-Sánchez, F.J. and Rodríguez Salgado, D. (2020), “Advances in Orthotic and Prosthetic Manufacturing: A Technology Review”, Materials (Basel), vol. 13(2), doi: https://doi.org/10.3390/ma13020295
Hesham A. B. (2021), “The effect of poor posture on the cervical range of motion in young subjects”, Egyptian Journal of Physical Therapy, vol. 5, no. 1, pp. 5–12, doi: https://doi.org/10.21608/ejpt.2020.35919.1010
Hlavcheva, D., Yaloveha, V., Podorozhniak, A. and Kuchuk, H. (2021), “Comparison of CNNs for Lung Biopsy Images Classification”, 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2021 – Proceedings, pp. 1–5, doi: https://doi.org/10.1109/UKRCON53503.2021.9575305
Hlavcheva, D., Yaloveha, V., Podorozhniak, A., and Kuchuk, H. (2020), “Tumor nuclei detection in histopathology images using R – CNN”, CEUR Workshop Proceedings, 2740, pp. 63–74, available at:
https://www.scopus.com/record/display.uri?eid=2-s2.0-85096413380&origin=resultslist
Kim, G.U., Park, W.T., Chang, M.C. and Lee, G.W. (2022), “Diagnostic Technology for Spine Pathology”, Asian Spine J., vol. 16(5), pp. 764–775, doi: https://doi.org/10.31616/asj.2022.0374
Harada, G.K., Siyaji, Z.K., Younis, S., Louie, P.K., Samartzis, D. and An H.S. (2019), “Imaging in Spine Surgery: Current Concepts and Future Directions”, Spine Surg Relat Res., vol. 4(2), pp. 99–110, doi: https://doi.org/10.22603/ssrr.2020-0011
Vutan, A. M., Olariu, K. P., Jurjiu, N. A. and Pantea, C. (2022), “The influence of the visual analyzer on posture and balance–review type study”, Studia Universitatis Babeș-Bolyai Educatio Artis Gymnasticae, pp. 217–224, doi: https://doi.org/10.24193/subbeag.67(4).50
Saboor, A., Kask, T., Kuusik, A., Alam, M. M., Le Moullec, Y., Niazi, I. K. and Ahmad, R. (2020), “Latest research trends in gait analysis using wearable sensors and machine learning: A systematic review”, Ieee Access, vol. 8, pp. 167830-167864, doi: https://doi.org/10f.1109/ACCESS.2020.3022818
Dashkov, D. and Liashenko, O. (2023), “Motion capture with mems sensors”, Advanced Information Systems, vol. 7, no. 2, pp. 57–62, doi: https://doi.org/10.20998/2522-9052.2023.2.08
Wu, H.D. and Wong, M.S. (2020), “Assessment of Maximum Spinal Deformity in Scoliosis: A Literature Review”, J. Med. Biol. Eng., vol. 40, pp. 621–629, doi: https://doi.org/10.1007/s40846-020-00558-z
Cheng, F., Lu, L., Sun, M., Wang, X. and Wang, Y. (2024), “Non-invasive Scoliosis Assessment in Adolescents”, Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning, ICMTEL 2023, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications, Engineering, vol 535, pp. 221–230, Springer, Cham, doi: https://doi.org/10.1007/978-3-031-50580-5_18
Amran, N.N., Basaruddin, K.S., Ijaz, M.F., Yazid, H., Basah, S.N., Muhayudin, N.A. and Sulaiman, A.R. (2023), “Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review”, Applied Sciences, vol. 13(20), 11555, doi: https://doi.org/10.3390/app132011555
Yeroshenko, O. and Prasol, I. (2022), “Simulation of the electrical signal of the muscles to obtain the electromiosignal spectrum”, Technology audit and production reserves, vol. 2, no. 2 (64), pp. 38–43, doi: https://doi.org/10.15587/2706-5448.2022.254566
Yeroshenko, O., Prasol, I. and Suknov, M. (2022), “Modeling of electrostimulation characteristics to determine the optimal amplitude of current stimuli”, Radioelectronic and computer systems, no. 2, pp. 191–199, doi: https://doi.org/10.32620/reks.2022.2.15
Zaitseva, E, Levashenko, V, Rabcan, J. and Kvassay, M. (2023), “A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine”, Bioengineering, vol. 10(7), 838, doi: https://doi.org/10.3390/bioengineering10070838
Levashenko, V., Zaitseva, E., Kvassay, M. and Deserno, T. (2016), “Reliability estimation of healthcare systems using Fuzzy Decision Trees”, Proc. of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS 2016), Gdansk, Poland, September 11-14, 2016, pp. 331–340, doi: https://doi.org/10.15439/2016F150
Yaloveha, V., Hlavcheva, D., Podorozhniak, A. and Kuchuk, H. (2019), “Fire hazard research of forest areas based on the use of convolutional and capsule neural networks”, 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2019 – Proceedings, doi: http://dx.doi.org/10.1109/UKRCON.2019.8879867
Zaitseva, E., Rabcan, J., Levashenko, V. and Kvassay, M. (2023), “Importance analysis of decision making factors based on fuzzy decision trees”, Applied Soft Computing, vol. 134, 109988, https://doi.org/10.1016/j.asoc.2023.109988
Parvaiz, A., Khalid, M. A., Zafar, R., Ameer, H., Ali, M., and Fraz, M. M. (2023), “Vision transformers in medical computer vision—A contemplative retrospection”, Engineering Applications of Artificial Intelligence, vol. 122, pp. 106–126, doi: https://doi.org/10.1016/j.engappai.2023.106126
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, vol. 1(4 (121), pp. 48–55, doi: https://doi.org/10.15587/1729-4061.2023.274177
Barkovska, O., Filippenko, I., Semenenko, I., Korniienko, V. and Sedlaček, P. (2023), “Adaptation of FPGA architecture for accelerated image preprocessing”, Radioelectronic and Computer Systems, vol. 2, pp. 94–106, doi: https://doi.org/10.32620/reks.2023.2.08
Barkovska, O., Bilichenko, O., Uvarov, G. and Makushenko Т. (2024), “Improved rendering method of skeletal animation on control points base”, Computer systems and information technologies, no. 1, pp. 71–81, doi: https://doi.org/10.31891/csit-2024-1-9
Goyushova, U. (2023), “Algorithms for finding non-intersecting roads on images”, Advanced Information Systems, vol. 7, no. 2, pp. 5–8, 2023, doi: https://doi.org/10.20998/2522-9052.2023.2.01