Recognition of human gait based on ground reaction forces and combined data from two gait laboratories

• Autorzy: Derlatka Marcin , Skublewska-Paszkowska Maria , Powroźnik Paweł , Smołka Jakub , Łukasik Edyta , Borysiewicz Agnieszka , Borkowski Piotr , Czerwiński Dariusz

Identyfikatory

DOI

10.2478/ama-2024-0040

• Języki publikacji: EN

Abstrakty

EN

In a world in which biometric systems are used more and more often within our surroundings while the number of publications related to this topic grows, the issue of access to databases containing information that can be used by creators of such systems becomes important. These types of databases, compiled as a result of research conducted by leading centres, are made available to people who are interested in them. However, the potential combination of data from different centres may be problematic. The aim of the present work is the verification of whether the utilisation of the same research procedure in studies carried out on research groups having similar characteristics but at two different centres will result in databases that may be used to recognise a person based on Ground Reaction Forces (GRF). Studies conducted for the needs of this paper were performed at the Bialystok University of Technology (BUT) and Lublin University of Technology (LUT). In all, the study sample consisted of 366 people allowing the recording of 6,198 human gait cycles. Based on obtained GRF data, a set of features describing human gait was compiled which was then used to test a system’s ability to identify a person on its basis. The obtained percentage of correct identifications, 99.46% for BUT, 100% for LUT and 99.5% for a mixed set of data demonstrates a very high quality of features and algorithms utilised for classification. A more detailed analysis of erroneous classifications has shown that mistakes occur most often between people who were tested at the same laboratory. Completed statistical analysis of select attributes revealed that there are statistically significant differences between values attained at different laboratories.

Słowa kluczowe

EN

human gait recognition; biometrics; ground reaction forces; databases

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2024
• Tom: Vol. 18, no. 2
• Strony: 361--366
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Derlatka Marcin

• nstitute of Biomedical Engineering, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0002-6736-5106

autor

Skublewska-Paszkowska Maria

• Department of Computer Science, Faculty of Electrical Engineering and Computer Science Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-0760-7126

autor

Powroźnik Paweł

• Department of Computer Science, Faculty of Electrical Engineering and Computer Science Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-5705-4785

autor

Smołka Jakub

• Department of Computer Science, Faculty of Electrical Engineering and Computer Science Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-8350-2537

autor

Łukasik Edyta

• Department of Computer Science, Faculty of Electrical Engineering and Computer Science Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0003-3644-9769

autor

Borysiewicz Agnieszka

• Department of Emergency Medicine, Faculty of Health Sciences, Medical University of Białystok, Szpitalna 37, 15-295 Bialystok, Poland

• https://orcid.org/0000-0001-7740-7800

autor

Borkowski Piotr

• nstitute of Biomedical Engineering, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska 45C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0001-9038-2601

autor

Czerwiński Dariusz

• Department of Applied Computer Science, Faculty of Mathematics and Information Technology, Lublin University of Technology, Nadbystrzycka 38B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-3642-1929

Bibliografia

• 1. Yang W, Wang S, Hu J, Zheng G, Valli C. Security and accuracy of fingerprint-based biometrics: A review. Symmetry (Basel). 2019; 11(2): 141. https://doi.org/10.3390/sym11020141
• 2. Lohr D, Komogortsev OV. Eye Know You Too: Toward Viable End-to-End Eye Movement Biometrics for User Authentication. IEEE Transactions on Information Forensics and Security. 2022;17:3151–64. https://doi.org/10.1109/TIFS.2022.3201369
• 3. Chen X, Li Z, Setlur S, Xu W. Exploring racial and gender disparities in voice biometrics. Scientific Reports. 2022; 12(1), 3723. https://doi.org/10.1038/s41598-022-06673-y
• 4. Stragapede G, Delgado-Santos P, Tolosana R, Vera-Rodriguez R, Guest R, Morales A. Mobile keystroke biometrics using transformers. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE. Waikoloa Beach, HI, USA 2023. 1-6. https://doi.org/10.1109/FG57933.2023.10042710
• 5. Taskiran M, Kahraman N, Erdem CE. Face recognition: Past, present and future (a review). Digital Signal Processing. 2020; 106: 102809. https://doi.org/10.1016/j.dsp.2020.102809
• 6. Parashar A, Parashar A, Ding W, Shekhawat RS, Rida I. Deep learning pipelines for recognition of gait biometrics with covariates: A comprehensive review. Artificial Intelligence Review. 2023; 1-65. https://doi.org/10.1007/s10462-022-10365-4
• 7. Szczuko P, Harasimiuk A, Czyżewski A. Evaluation of decision fusion methods for multimodal biometrics in the banking application. Sen-sors. 2022; 22(6): 2356. https://doi.org/10.3390/s22062356
• 8. Ren H, Sun L, Guo J, Han C. A dataset and benchmark for multi-modal biometric recognition based on fingerprint and finger vein. IEEE Transactions on Information Forensics and Security. 2022; 17: 2030-2043. https://doi.org/10.1109/TIFS.2022.3175599
• 9. Delgado-Santos P, Tolosana R, Guest R, Deravi F, Vera-Rodriguez R. Exploring transformers for behavioural biometrics: A case study in gait recognition. Pattern Recognition. 2023; 143: 109798. https://doi.org/10.1016/j.patcog.2023.109798
• 10. Rani V, Kumar M. Human gait recognition: A systematic review. Multimedia Tools and Applications. 2023; 1-35. https://doi.org/10.1007/s11042-023-15079-5
• 11. Horst F, Slijepcevic D, Simak M, Schöllhorn WI. Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals. Scientific data. 2021; 8(1): 232. https://doi.org/10.1038/s41597-021-01014-6
• 12. Derlatka M, Parfieniuk M. Real-world measurements of ground reaction forces of normal gait of young adults wearing various foot-wear. Scientific data. 2023; 10(1): 60. https://doi.org/10.1038/s41597-023-01964-z
• 13. Makihara Y, Nixon MS, Yagi Y. Gait recognition: Databases, repre-sentations, and applications. Computer Vision: A Reference Guide. 2020; 1-13. https://doi.org/10.1007/978-3-030-03243-2_883-1
• 14. Song C, Huang Y, Wang W, Wang L. CASIA-E: a large comprehen-sive dataset for gait recognition. IEEE Transactions on Pattern Anal-ysis and Machine Intelligence. 2022; 45(3): 2801-2815. https://doi.org/10.1109/TPAMI.2022.3183288
• 15. Ngo TT, Ahad MAR, Antar AD, Ahmed M, Muramatsu D, Makihara Y, et al. OU-ISIR wearable sensor-based gait challenge: Age and gen-der. In 2019 International Conference on Biometrics (ICB). 2019; 1-6. IEEE. https://doi.org/10.1109/ICB45273.2019.8987235
• 16. Malekzadeh M, Clegg RG, Cavallaro A, Haddadi H. Protecting sen-sory data against sensitive inferences. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems. 2018; 1-6. https://doi.org/10.1145/3195258.3195260
• 17. Zou Q, Wang Y, Wang Q, Zhao Y, Li Q. Deep learning-based gait recognition using smartphones in the wild. IEEE Transactions on In-formation Forensics and Security. 2020; 15: 3197-3212. https://doi.org/ 10.1109/TIFS.2020.2985628
• 18. Tan D, Huang K, Yu S, Tan T. (2006, August). Efficient night gait recognition based on template matching. In 18th International Con-ference on Pattern Recognition (ICPR'06). IEEE. 2006; 3: 1000-1003. https://doi.org/10.1109/ICPR.2006.478
• 19. Yu S, Tan D, Tan T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In 18th In-ternational Conference on Pattern Recognition (ICPR'06). 2006; 4: 441-444. IEEE. https://doi.org/10.1109/ICPR.2006.67
• 20. Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW. The humanID gait challenge problem: Data sets, performance, and anal-ysis. IEEE Transactions on Pattern Analysis and Machine Intelli-gence. 2005; 27(2): 162-177. https://doi.org/10.1109/TPAMI.2005.39
• 21. Smith T, Ditroilo M. Force plate coverings significantly affect meas-urement of ground reaction forces. Plos one. 2023; 18(11): e0293959. https://doi.org/10.1371/journal.pone.0293959
• 22. Horst F, Slijepcevic D, Simak M, Horsak B, Schöllhorn WI, Zep-pelzauer M. Modeling Biological Individuality Using Machine Learn-ing: A Study on Human Gait. Computational and Structural Biotech-nology Journal. 2023; 21:3414-3423 https://doi.org/10.1016/j.csbj.2023.06.009
• 23. Derlatka M, Borowska M. Ensemble of heterogeneous base classifi-ers for human gait recognition. Sensors, 2023; 23(1): 508. https://doi.org/10.3390/s23010508
• 24. Guo Y, Hastie T, Tibshirani R. Regularized linear discriminant analy-sis and its application in microarrays. Biostatistics. 2007; 8:86–100. https://doi.org/10.1093/biostatistics/kxj035.
• 25. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014. doi:10.48550/arXiv.1412.6980
• 26. Derlatka M, Bogdan M. Recognition of a person wearing sport shoes or high heels through gait using two types of sensors. Sensors. 2018; 18(5): 1639. https://doi.org/10.3390/s18051639
• 27. Duncanson K, Thwaites S, Booth D, Abbasnejad E, Robertson WS, Thewlis D. The most discriminant components of force platform data for gait based person re-identification. 2021. https://doi.org/10.36227/techrxiv, 16683229, v1