References
- C. Prakash, R. Kumar, and N. Mittal, “Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges,” Artificial Intelligence Review, vol. 49, no. 1, pp. 1–40, Sep. 2018. https://doi.org/10.1007/s10462-016-9514-6
- C. Fan et al., “GaitPart: Temporal part-based model for gait recognition,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, Jun. 2020, pp. 14213–14221. https://doi.org/10.1109/CVPR42600.2020.01423
- X. Huang et al., “Context-sensitive temporal feature learning for gait recognition,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, Oct. 2021, pp. 12889–12898. https://doi.org/10.1109/ICCV48922.2021.01267
- H. Li et al., “GaitSlice: A gait recognition model based on spatio-temporal slice features,” Pattern Recognition, vol. 124, Apr. 2022, Art. no. 108453. https://doi.org/10.1016/j.patcog.2021.108453
- X. Huang, X. Wang, B. He, S. He, W. Liu, and B. Feng, “STAR: Spatio-temporal augmented relation network for gait recognition,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5, no. 1, pp. 115–125, Jan. 2023. https://doi.org/10.1109/TBIOM.2022.3211843
- C. Fan, J. Liang, C. Shen, S. Hou, Y. Huang, and S. Yu, “OpenGait: Revisiting gait recognition toward better practicality,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, Jun. 2023, pp. 9707–9716. https://doi.org/10.1109/CVPR52729.2023.00936
- T. T. Verlekar, P. Lobato Correia, and L. D. Soares, “Using transfer learning for classification of gait pathologies,” in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, Dec. 2018, pp. 2376–2381. https://doi.org/10.1109/BIBM.2018.8621302
- R. Kaur, S. Menon, X. Zhang, R. Sowers, and M. E. Hernandez, “Exploring characteristic features in gait patterns for predicting multiple sclerosis,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, Jul. 2019, pp. 4217–4220. https://doi.org/10.1109/EMBC.2019.8857604
- R. Kaur, R. W. Motl, R. Sowers, and M. E. Hernandez, “A vision-based framework for predicting multiple sclerosis and Parkinson’s disease gait dysfunctions – A deep learning approach,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 190–201, Jan. 2023. https://doi.org/10.1109/JBHI.2022.3208077
- V. W. S. Tan, W. X. Ooi, Y. F. Chan, T. Connie, and M. K. O. Goh, “Vision-based gait analysis for neurodegenerative disorders detection,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 136–154, Feb. 2024. https://doi.org/10.33093/jiwe.2024.3.1.9
- J. Stenum, M. M. Hsu, A. Y. Pantelyat, and R. T. Roemmich, “Clinical gait analysis using video-based pose estimation: multiple perspectives, clinical populations, and measuring change,” PLOS Digital Health, vol. 3, no. 3, 2024, Art. no. e0000467. https://doi.org/10.1371/journal.pdig.0000467
- X. Li, H. Xu, and J. T. Cheung, “Gait-force model and inertial measurement unit-based measurements: A new approach for gait analysis and balance monitoring,” Journal of Exercise Science & Fitness, vol. 14, no. 2, pp. 60–66, Dec. 2016. https://doi.org/10.1016/j.jesf.2016.07.002
- H. Zhang, Y. Guo, and D. Zanotto, “Accurate ambulatory gait analysis in walking and running using machine learning models,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 1, pp. 191–202, Jan. 2020. https://doi.org/10.1109/TNSRE.2019.2958679
- L. Xiang, Y. Gu, Q. Mei, A. Wang, V. Shim, and J. Fernandez, “Automatic classification of barefoot and shod populations based on the foot metrics and plantar pressure patterns,” Frontiers in Bioengineering and Biotechnology, vol. 10, Mar. 2022, Art. no. 843204. https://doi.org/10.3389/fbioe.2022.843204
- D. Adolph, W. Tschacher, H. Niemeyer, and J. Michalak, “Gait patterns and mood in everyday life: A comparison between depressed patients and non-depressed controls,” Cognitive Therapy and Research, vol. 45, pp. 1128–1140, Feb. 2021. https://doi.org/10.1007/s10608-021-10215-7
- Y. Wang, J. Wang, X. Liu, and T. Zhu, “Detecting depression through gait data: examining the contribution of gait features in recognizing depression,” Frontiers in Psychiatry, vol. 12, May 2021, Art. no. 661213. https://doi.org/10.3389/fpsyt.2021.661213
- Y. Wen, B. Li, X. Liu, D. Chen, S. Gao, and T. Zhu, “Using gait videos to automatically assess anxiety,” Frontiers in Public Health, vol. 11, Mar. 2023, Art. no. 1082139. https://doi.org/10.3389/fpubh.2023.1082139
- J. P. Singh, S. Jain, S. Arora, and U. P. Singh, “Vision-based gait recognition: A survey,” IEEE Access, vol. 6, pp. 70497–70527, Nov. 2018. https://doi.org/10.1109/ACCESS.2018.2879896
- M. H. Khan, M. S. Farid, and M. Grzegorzek, “Vision-based approaches towards person identification using gait,” Computer Science Review, vol. 42, Nov. 2021, Art. no. 100432. https://doi.org/10.1016/j.cosrev.2021.100432
- C. Song, Y. Huang, W. Wang, and L. Wang, “CASIA-E: A large comprehensive dataset for gait recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2801–2815, 2022. https://doi.org/10.1109/TPAMI.2022.3183288
- G. Zhao, G. Liu, H. Li, and M. Pietikainen, “3D gait recognition using multiple cameras,” in 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK, 2006, pp. 529–534. https://doi.org/10.1109/FGR.2006.2
- J. D. Shutler, M. G. Grant, M. S. Nixon, and J. N. Carter, “On a large sequence-based human gait database,” in Applications and Science in Soft Computing. Berlin, Germany: Springer, 2004, pp. 339–346. https://doi.org/10.1007/978-3-540-45240-9_46
- S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer, “The humanID gait challenge problem: data sets, performance, and analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 162–177, Feb. 2005. https://doi.org/10.1109/TPAMI.2005.39
- S. Yu, D. Tan, and T. Tan, “A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition,” in 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, China, Aug. 2006, pp. 441–444. https://doi.org/10.1109/ICPR.2006.67
- H. Iwama, M. Okumura, Y. Makihara, and Y. Yagi, “The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 5, pp. 1511–1521, Oct. 2012. https://doi.org/10.1109/TIFS.2012.2204253
- N. Takemura, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, “Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition,” IPSJ Transactions on Computer Vision and Applications, vol. 10, no. 4, pp. 1–14, Feb. 2018. https://doi.org/10.1186/s41074-018-0039-6
- Z. Mu, F. M. Castro, M. J. Marín-Jiménez, N. Guil, Y.-R. Li, and S. Yu, “ReSGait: The real-scene gait dataset,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), Shenzhen, China, Aug. 2021, pp. 1–8. https://doi.org/10.1109/IJCB52358.2021.9484347
- M. Nieto-Hidalgo, F. J. Ferrández-Pastor, R. J. Valdivieso-Sarabia, J. Mora-Pascual, and J. M. García-Chamizo, “A vision based proposal for classification of normal and abnormal gait using RGB camera,” Journal of Biomedical Informatics, vol. 63, pp. 82–89, Oct. 2016. https://doi.org/10.1016/j.jbi.2016.08.003
- D. Goyal, K. Rao Jerripothula, and A. Mittal, “Detection of gait abnormalities caused by neurological disorders,” in 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland, Sep. 2020, pp. 1–6. https://doi.org/10.1109/MMSP48831.2020.9287163
- A. Dadashzadeh, A. Whone, M. Rolinski, and M. Mirmehdi, “Exploring motion boundaries in an end-to-end network for vision-based Parkinson’s severity assessment,” arXiv preprint arXiv:2012.09890, Dec. 2020. https://doi.org/10.48550/arXiv.2012.09890
- R. Ranjan, D. Ahmedt-Aristizabal, M. A. Armin, and J. Kim, “Computer vision for clinical gait analysis: A gait abnormality video dataset,” arXiv preprint arXiv:2407.04190, Jul. 2024. https://doi.org/10.48550/arXiv.2407.04190
- H. Dou, W. Zhang, P. Zhang, Y. Zhao, S. Li, Z. Qin, F. Wu, L. Dong, and X. Li, “VersatileGait: A large-scale synthetic gait dataset with fine-grained attributes and complicated scenarios,” arXiv preprint arXiv:2101.01394, Jan. 2021. https://doi.org/10.48550/arXiv.2101.01394
- Y. Makihara et al., “Gait collector: An automatic gait data collection system in conjunction with an experience-based long-run exhibition,” in 2016 International Conference on Biometrics (ICB), Halmstad, Sweden, Jun. 2016, pp. 1–8. https://doi.org/10.1109/ICB.2016.7550090
- M. Baldinger, L. M. Reimer, and V. Senner, “Influence of the camera viewing angle on OpenPose validity in motion analysis,” Sensors, vol. 25, no. 3, Jan. 2025, Art. no. 799. https://doi.org/10.3390/s25030799
- X. Han, D. Guffanti, and A. Brunete, “A comprehensive review of vision-based sensor systems for human gait analysis,” Sensors, vol. 25, no. 2, Jan. 2025, Art. no. 498. https://doi.org/10.3390/s25020498
- R. Raman, P. K. Sa, S. Bakshi, and B. Majhi, “Towards optimized placement of cameras for gait pattern recognition,” Procedia Technology, vol. 6, pp. 1019–1025, 2012. https://doi.org/10.1016/j.protcy.2012.10.124
- M. Riberto, R. F. Liporaci, O. F. Vieira, and J. B. Volpon, “Setting up a human motion analysis laboratory: Camera positioning for kinematic recording of gait,” International Journal of Physical Medicine & Rehabilitation, vol. 1, no. 4, pp. 1–3, 2013. https://doi.org/10.4172/2329-9096.1000131
- M. W. Whittle, “Gait analysis,” in The Soft Tissues, G. R. McLatchie and C. M. E. Lennox, Eds. Oxford, U.K.: Butterworth-Heinemann, 1993, pp. 187–199. https://doi.org/10.1016/B978-0-7506-0170-2.50017-0
- H. Schulzrinne, A. Rao, and R. Lanphier, “Real time streaming protocol (RTSP),” RFC 2326, Apr. 1998. https://doi.org/10.17487/rfc2326
- S.-j. Sun and X.-d. Yang, “Method of CCD camera calibration based on OpenCV,” in 2015 International Conference on Industrial Informatics -Computing Technology, Intelligent Technology, Industrial Information Integration, Wuhan, China, Dec. 2015, pp. 34–37. https://doi.org/10.1109/ICIICII.2015.96
- A. Dutta, R. Lal, D. S. Raychaudhuri, C.-K. Ta and A. K. Roy-Chowdhury, “POISE: Pose guided human silhouette extraction under occlusions,” in 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, Jan. 2024, pp. 6141–6151. https://doi.org/10.1109/WACV57701.2024.00604
- K. H. Karstensen, “Silhouette extraction using graphics processing units,” M.S. thesis, 2012.
- J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91
- G. Jocher and J. Qiu, “Ultralytics YOLO11,” GitHub repository, 2024. [Online]. Available: https://github.com/ultralytics
- R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. Hoboken, NJ, USA: Pearson, 2018.
- N. Ravi et al., “Sam 2: Segment anything in images and videos,” arXiv preprint arXiv:2408.00714, Aug. 2024. https://doi.org/10.48550/arXiv.2408.00714
- L. C. Chen, “Rethinking Atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587, 2017. https://arxiv.org/pdf/1706.05587
- A. Kirillov et al., “Segment anything,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, Oct. 2023, pp. 3992–4003. https://doi.org/10.1109/ICCV51070.2023.00371
- S. Fritz and M. Lusardi, “Walking speed: The sixth vital sign,” Journal of Geriatric Physical Therapy, vol. 32, no. 2, pp. 2–5, 2009. https://doi.org/10.1519/00139143-200932020-00002
- A. W. Andrews, S. A. Chinworth, M. Bourassa, M. Garvin, D. Benton, and S. Tanner, “Update on distance and velocity requirements for community ambulation,” Journal of Geriatric Physical Therapy, vol. 33, no. 3, pp. 128–134, 2010.