Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision
Abstract
:1. Introduction
2. Related Work
3. Methodologies
3.1. Model Architecture and Design
3.2. Scenario-Driven Improved YOLO Algorithm
3.3. Head Movement Estimation Algorithm
3.4. Face Encryption Model
4. Experiment
4.1. Construction of Cervical Spine Operation Dataset
4.2. Analysis of Standard Cervical Spine Exercises
5. Results and Discussion
5.1. Scoring System
5.2. Experiment Results
5.3. Analysis of Experiment Results
5.4. Ablation Study
5.5. Discussion of the Mosaic Encryption Effect
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Yongjun, Z.; Tingjie, Z.; Xiaoqiu, Y.; Zhiying, F.; Feng, Q.; Guangke, X.; Jinfeng, L.; Fachuan, N.; Xiaohong, J.; Yanqing, L. A survey of chronic pain in China. Libyan J. Med. 2020, 15, 1. [Google Scholar]
- Singh, N.N.; Bawa, P.S. Cervical spondylosis in adolescents: A review of the literature. Asian Spine J. 2014, 8, 704–710. [Google Scholar]
- Mummaneni, P.V.; Ames, C.P. Cervical spondylosis in young adults: A review of the literature. Neurosurg. Focus 2009, 27, E6. [Google Scholar]
- Gautam, D.K.; Shrestha, M.K. Cervical spondylosis in adolescents: Clinical and radiographic evaluation. J. Orthop. Surg. 2017, 25, 2309499016682792. [Google Scholar]
- Baker, J.C.; Glotzbecker, M.P. Cervical spondylosis in adolescents: A rare cause of neck pain. J. Pediatr. Orthop. 2014, 34, e49–e51. [Google Scholar]
- Elawady, M.E.M.; El-Sabbagh, M.H.; Eldesouky, S.S. Cervical spondylosis in adolescents: A case report. J. Orthop. Case Rep. 2016, 6, 56–59. [Google Scholar]
- O’Riordan, C.; Clifford, A.; Van De Ven, P.; Nelson, J. Chronic neck pain and exercise interventions: Frequency, intensity, time, and type principle. Arch. Phys. Med. Rehabil. 2014, 95, 770–783. [Google Scholar]
- Liu, L.; Ke, Z.; Huo, J.; Chen, J. Head pose estimation through keypoints matching between reconstructed 3D face model and 2D image. Sensors 2021, 21, 1841. [Google Scholar]
- Ranjan, R.; Patel, V.M.; Chellappa, R. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 41, 121–135. [Google Scholar]
- Guo, J.; Zhu, X.; Yang, Y.; Yang, F.; Lei, Z.; Li, S.Z. Towards fast, accurate and stable 3d dense face alignment. In Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23–28 August 2020; pp. 152–168. [Google Scholar]
- Xin, M.; Mo, S.; Lin, Y. Eva-gcn: Head pose estimation based on graph convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 1462–1471. [Google Scholar]
- Kumar, A.; Alavi, A.; Chellappa, R. KEPLER: Simultaneous estimation of keypoints and 3D pose of unconstrained faces in a unified framework by learning efficient H-CNN regressors. Image Vis. Comput. 2018, 79, 49–62. [Google Scholar] [CrossRef]
- Ruiz, N.; Chong, E.; Rehg, J.M. Fine-grained head pose estimation without keypoints. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–22 June 2018; pp. 2074–2083. [Google Scholar]
- Huang, B.; Chen, R.; Xu, W.; Zhou, Q. Improving head pose estimation using two-stage ensembles with top-k regression. Image Vis. Comput. 2020, 93, 103827. [Google Scholar]
- Cao, Z.; Chu, Z.; Liu, D.; Chen, Y. A vector-based representation to enhance head pose estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 3–8 January 2021; pp. 1188–1197. [Google Scholar]
- Dhingra, N. LwPosr: Lightweight Efficient Fine Grained Head Pose Estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 3–8 January 2022; pp. 1495–1505. [Google Scholar]
- Thai, C.; Tran, V.; Bui, M.; Ninh, H.; Tran, H.Y. An Effective Deep Network for Head Pose Estimation without Keypoints. In Proceedings of the ICPRAM, Online, 3–5 February 2022. [Google Scholar]
- Alioua, N.; Amine, A.; Rogozan, A.; Bensrhair, A.; Rziza, M. Driver head pose estimation using efficient descriptor fusion. EURASIP J. Image Video Process. 2016, 2016, 2. [Google Scholar]
- Paone, J.; Bolme, D.; Ferrell, R.; Aykac, D.; Karnowski, T. Baseline face detection, head pose estimation, and coarse direction detection for facial data in the SHRP2 naturalistic driving study. In Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Republic of Korea, 28 June–1 July 2015; pp. 174–179. [Google Scholar]
- Yücel, Z.; Salah, A.A. Head pose and neural network based gaze direction estimation for joint attention modeling in embodied agents. In Proceedings of the Annual Meeting of Cognitive Science Society, Amsterdam, The Netherlands, 29 July–1 August 2009. [Google Scholar]
- Akrout, B.; Fakhfakh, S. Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera. Math. Probl. Eng. 2020, 2020, 9830672. [Google Scholar]
- Hassner, T.; Harel, S.; Paz, E.; Enbar, R. Effective face frontalization in unconstrained images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Patna, India, 16–19 December 2015; pp. 4295–4304. [Google Scholar]
- Barra, P.; Barra, S.; Bisogni, C.; De Marsico, M.; Nappi, M. Web-shaped model for head pose estimation: An approach for best exemplar selection. IEEE Trans. Image Process. 2020, 29, 5457–5468. [Google Scholar] [CrossRef]
- Miyoshi, K.; Nomiya, H.; Hochin, T. Detection of Dangerous Behavior by Estimation of Head Pose and Moving Direction. In Proceedings of the 2018 5th International Conference on Computational Science/Intelligence and Applied Informatics (CSII), Yonago, Japan, 10–12 July 2018; pp. 121–126. [Google Scholar]
- Kartynnik, Y.; Ablavatski, A.; Grishchenko, I.; Grundmann, M. Real-time facial surface geometry from monocular video on mobile GPUs. arXiv 2019, arXiv:1907.06724. [Google Scholar]
- Xiong, J.; Wang, Y. Study on the Prescription of Acupuncture in the Treatment of Cervical Spondylotic Radiculopathy Based on Computer Vision Image Analysis. Contrast Media Mol. Imaging 2022, 2022, 8121636. [Google Scholar] [PubMed]
- Murugaviswanathan, R.; D, M.; Subramaniyam, M.; Min, S. Measurement of Spine Curvature using Flexicurve Integrated with Machine Vision. Hum. Factors Wearable Technol. 2022, 29, 82–86. [Google Scholar] [CrossRef]
- Jiang, J. Research on the improved image tracking algorithm of athletes’ cervical health. Rev. Bras. Med. Esporte 2021, 27, 476–480. [Google Scholar]
- Wang, Y.; Sun, D.; Liu, L.; Ye, L.; Fu, K.; Jin, X. Detection of cervical vertebrae from infrared thermal imaging based on improved Yolo v3. In Proceedings of the 5th International Conference on Computer Science and Software Engineering, Guilin, China, 21–23 October 2022; pp. 312–318. [Google Scholar]
- Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788. [Google Scholar]
- Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 7263–7271. [Google Scholar]
- Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767. [Google Scholar]
- Bochkovskiy, A.; Wang, C.Y.; Liao, H.Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv 2020, arXiv:2004.10934. [Google Scholar]
- Ge, Z.; Liu, S.; Wang, F.; Li, Z.; Sun, J. Yolox: Exceeding yolo series in 2021. arXiv 2021, arXiv:2107.08430. [Google Scholar]
- meituan. YOLOv6. 2022. Available online: https://github.com/meituan/YOLOv6/ (accessed on 31 July 2022).
- ultralytics. YOLOv5. 2022. Available online: https://github.com/ultralytics/yolov5/ (accessed on 31 July 2022).
- Yu, J.; Jiang, Y.; Wang, Z.; Cao, Z.; Huang, T. Unitbox: An advanced object detection network. In Proceedings of the 24th ACM international conference on Multimedia, Amsterdam, The Netherlands, 15–19 October 2016; pp. 516–520. [Google Scholar]
- Zheng, Z.; Wang, P.; Liu, W.; Li, J.; Ye, R.; Ren, D. Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 12993–13000. [Google Scholar]
- TECHNOGYM. The Neck—The Power Behind the Head. 2022. Available online: https://www.technogym.com/us/wellness/the-neck-the-power-behind-the-head/ (accessed on 25 July 2022).
- Radmehr, A.; Asgari, M.; Masouleh, M.T. Experimental Study on the Imitation of the Human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot with ROS and Mediapipe Framework. In Proceedings of the 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, 17–19 November 2021; pp. 472–478. [Google Scholar]
- Lugaresi, C.; Tang, J.; Nash, H.; McClanahan, C.; Uboweja, E.; Hays, M.; Zhang, F.; Chang, C.L.; Yong, M.G.; Lee, J.; et al. Mediapipe: A framework for building perception pipelines. arXiv 2019, arXiv:1906.08172. [Google Scholar]
- Aşkin, A.; BAYRAM, K.B.; Demirdal, Ü.S.; Atar, E.; Karaman, Ç.A.; Güvendi, E.; Tosun, A. The evaluation of cervical spinal angle in patients with acute and chronic neck pain. Turk. J. Med Sci. 2017, 47, 806–811. [Google Scholar] [CrossRef]
- Sukari, A.A.A.; Singh, S.; Bohari, M.H.; Idris, Z.; Ghani, A.R.I.; Abdullah, J.M. Examining the range of motion of the cervical spine: Utilising different bedside instruments. Malays. J. Med. Sci. MJMS 2021, 28, 100. [Google Scholar] [PubMed]
- Moreno, A.J.; Utrilla, G.; Marin, J.; Marin, J.J.; Sanchez-Valverde, M.B.; Royo, A.C. Cervical spine assessment using passive and active mobilization recorded through an optical motion capture. J. Chiropr. Med. 2018, 17, 167–181. [Google Scholar]
- Ohberg, F.; Grip, H.; Wiklund, U.; Sterner, Y.; Karlsson, J.S.; Gerdle, B. Chronic whiplash associated disorders and neck movement measurements: An instantaneous helical axis approach. IEEE Trans. Inf. Technol. Biomed. 2003, 7, 274–282. [Google Scholar] [PubMed]
- Lind, B.; Sihlbom, H.; Nordwall, A.; Malchau, H. Normal range of motion of the cervical spine. Arch. Phys. Med. Rehabil. 1989, 70, 692–695. [Google Scholar] [PubMed]
x1 | x2 | y1 | y2 | z1 | z2 | |
---|---|---|---|---|---|---|
a | 2.25 | 2.82 | 2.90 | 2.82 | 2.90 | 2.82 |
b | 3.06 | 3.09 | 2.75 | 2.58 | 2.75 | 2.58 |
Pitch/x (Degree) | Yaw/y (Degree) | Roll/z (Degree) | |
---|---|---|---|
Male | 68 | 145 | 45 |
Female | 76 | 139 | 45 |
RMSE | MAE | R | KT | RBO | |
---|---|---|---|---|---|
angle_all | 16.353 | 15.879 | −6.220 | 0.9111 | 0.975 |
angular_speed_all | 15.797 | 15.192 | −5.737 | 0.645 | 0.853 |
angle_x + angular_speed_x | 7.307 | 6.280 | −0.442 | 0.2889 | 0.708 |
angle_y + angular_speed_y | 2.964 | 2.492 | 0.763 | 0.644 | 0.892 |
angle_z + angular_speed_z | 7.328 | 5.917 | −0.450 | 0.689 | 0.862 |
angle_xy + angular_speed_xy | 4.214 | 3.325 | 0.521 | 0.511 | 0.733 |
angle_xz + angular_speed_xz | 1.649 | 1.356 | 0.927 | 0.733 | 0.908 |
angle_yz + angular_speed_yz | 3.150 | 2.672 | 0.732 | 0.867 | 0.957 |
angle_all + angular_speed_all (Ours) | 0.765 | 0.586 | 0.984 | 0.9111 | 0.975 |
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Du, Q.; Bai, H.; Zhu, Z. Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision. Sensors 2023, 23, 3825. https://doi.org/10.3390/s23083825
Du Q, Bai H, Zhu Z. Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision. Sensors. 2023; 23(8):3825. https://doi.org/10.3390/s23083825
Chicago/Turabian StyleDu, Qiwei, Heting Bai, and Zhongpan Zhu. 2023. "Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision" Sensors 23, no. 8: 3825. https://doi.org/10.3390/s23083825
APA StyleDu, Q., Bai, H., & Zhu, Z. (2023). Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision. Sensors, 23(8), 3825. https://doi.org/10.3390/s23083825