Speed Climbing Analysis System Based on Spatial Positioning and Posture Recognition: Design and Effectiveness Assessment
Source abstract
Abstract
The human body posture and trajectory are important parameters of the optimal path in speed climbing, and current researchers are focused on them. However, the performance of the newly developed analysis tools for synchronously and accurately analyzing climbing posture and trajectory is limited. This study develops an innovative speed climbing analysis system (SCAS) that integrates three-dimensional trajectory tracking using HTC Vive trackers and full-body posture capture with BlazePose. And the system is validated. Climbing trials were recorded from twelve professional athletes (speed climbers, eight males and four females; age 22 ± 2.2 years, all with ≥1 year of competitive experience) on a standard International Federation of Sport Climbing (IFSC) speed wall. The SCAS’s accuracy was analyzed by comparing its trajectory measurements to a video-based reference: the mean deviation was 0.061 ± 0.005 m (mean ± SD, 95% confidence interval [0.058, 0.064] m), indicating high precision. Trajectory metrics between genders were compared using independent-sample t-tests, revealing that male climbers had significantly shorter average path lengths (p < 0.05) and fewer movement inflections than female climbers. Finally, the group-optimal path derived from the data showed only slight deviations from the top-performing climbers’ paths. The proposed SCAS enables synchronous, millimeter-level tracking of climbing trajectory and posture, and can provide coaches with quantitative feedback for each athlete’s climbing strategy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gaze Tracking and Assistive Technology · Human Pose and Action Recognition · Hand Gesture Recognition Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Key Laboratory of Intelligence Integrated Automation, Guangxi University, Guilin 541004, China
Shenzhen RunYiTaiYi Technology Co., Ltd., Shenzhen 518055, China
Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Funding
Shenzhen Science and Technology Program · National Natural Science Foundation of China · Shenzhen Medical Research Fund
References (43)
Show source-provided references
1. Competitive Performance Predictors in Speed Climbing, Bouldering, and Lead Climbing10.1080/02640414.2023.2239598 ↗
2. Road to Paris 2024: Force-Velocity Profile in Different Speed Climbers’ Abilities10.5114/biolsport.2024.131824 ↗
3. Olympic Competition Climbing: The Beginning of a New Era—A Narrative Review10.1136/bjsports-2020-102035 ↗
4. Strength and Conditioning Considerations for Speed Climbing10.1519/SSC.0000000000000745 ↗
5. Towards Statistical Analysis of Predictive Parameters in Competitive Speed Climbing10.1007/s12283-023-00431-7 ↗
6. Coordination in Climbing: Effect of Skill, Practice and Constraints Manipulation10.1007/s40279-015-0417-5 ↗
7. Langer, K., Simon, C., and Wiemeyer, J. (2023). Physical Performance Testing in Climbing—A Systematic Review. Front. Sports Act. Living, 5.10.3389/fspor.2023.1130812 ↗
8. Self-Reported Ability Assessment in Rock Climbing10.1080/02640414.2011.565362 ↗
9. Seifert, L., Cordier, R., Orth, D., Courtine, Y., and Croft, J.L. (2017). Role of Route Previewing Strategies on Climbing Fluency and Exploratory Movements. PLoS ONE, 12.10.1371/journal.pone.0176306 ↗
10. Kinematic Analysis of the Speed Climbing Event at the 2018 Youth Olympic Games10.1080/10255842.2020.1714907 ↗
11. Anthropometric, Strength, Endurance and Flexibility Characteristics of Elite and Recreational Climbers10.1080/02640419608727715 ↗
12. Raising the Bar in Sports Performance Research10.1080/02640414.2021.2024334 ↗
13. Badiola-Bengoa, A., and Mendez-Zorrilla, A. (2021). A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise. Sensors, 21.10.3390/s21185996 ↗
14. Yan, M., Wang, X., Dai, Y., Shen, S., Wen, C., Xu, L., Ma, Y., and Wang, C. (2023, January 17–24). CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-Scene Interactions. Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.10.1109/CVPR52729.2023.01247 ↗
15. Richter, J., Beltrán, R.B., and Heinkel, U. (2020). Camera-Based Climbing Analysis for a Therapeutic Training System. Curr. Dir. Biomed. Eng., 6.10.1515/cdbme-2020-2010 ↗
16. Pandurevic, D., Draga, P., Sutor, A., and Hochradel, K. (2022). Analysis of Competition and Training Videos of Speed Climbing Athletes Using Feature and Human Body Keypoint Detection Algorithms. Sensors, 22.10.3390/s22062251 ↗
17. Chen, R., Liu, Z., Li, Y., and Gao, J. (2022). A Time-Motion and Error Analysis of Speed Climbing in the 2019 IFSC Speed Climbing World Cup Final Rounds. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19106003 ↗
18. Askari Hosseini, S., and Wolf, P. (2023). Performance Indicators in Speed Climbing: Insights from the Literature Supplemented by a Video Analysis and Expert Interviews. Front. Sports Act. Living, 5.10.3389/fspor.2023.1304403 ↗
19. Reveret, L., Chapelle, S., Quaine, F., and Legreneur, P. (2020). 3D Visualization of Body Motion in Speed Climbing. Front. Psychol., 11.10.3389/fpsyg.2020.02188 ↗
20. Iguma, H., Kawamura, A., and Kurazume, R. (2020, January 12–15). A New 3D Motion and Force Measurement System for Sport Climbing. Proceedings of the 2020 IEEE/SICE International Symposium on System Integration (SII), Honolulu, HI, USA.10.1109/SII46433.2020.9026213 ↗
21. Shiao, Y., Chen, G.-Y., and Hoang, T. (2024). Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor. Sensors, 24.10.3390/s24134306 ↗
22. Measurement of the Positions of Light Sensors on a Mobile Frame Being Tracked with a Lighthouse Localization System10.1016/j.sna.2021.112979 ↗
23. Vrzáková, H., Koskinen, J., Andberg, S., Lee, A., and Amon, M.J. (2024). Towards Automatic Object Detection and Activity Recognition in Indoor Climbing. Sensors, 24.10.3390/s24196479 ↗
24. Xie, Y., Li, S., and Lin, P. (2023). Producing a Standard Dataset of Speed Climbing Training Videos Using Deep Learning Techniques. arXiv.
25. Kosourikhina, V., Kavanagh, D., Richardson, M.J., and Kaplan, D.M. (2022). Validation of Deep Learning-Based Markerless 3D Pose Estimation. PLoS ONE, 17.10.1101/2022.03.29.486170 ↗
26. DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning10.1038/s41593-018-0209-y ↗
27. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Trans10.1109/TPAMI.2019.2929257 ↗
28. Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., and Grundmann, M. (2020). BlazePose: On-Device Real-Time Body Pose Tracking. arXiv.
29. Stenum, J., Rossi, C., and Roemmich, R.T. (2021). Two-Dimensional Video-Based Analysis of Human Gait Using Pose Estimation. PLoS Comput. Biol., 17.10.1371/journal.pcbi.1008935 ↗
30. Deep Learning-Based Human Pose Estimation: A Survey10.1145/3603618 ↗
31. Markerless Multi-View 3D Human Pose Estimation: A Survey10.1016/j.imavis.2025.105437 ↗
32. Adaptability and Flexibility of the Human Motor System:Implications for Neurological Rehabilitation10.1155/NP.2001.131 ↗
33. A Perspective on the Diversity of Human Adaptability10.2114/jpa.24.243 ↗
34. Dynamical systems theory: A relevant framework for performance-oriented sports biomechanical research
35. International Federation of Sport Climbing (IFSC) (2025, April 10). Speed Licence Rules—Speed Walls. IFSC. Available online: https://www.ifsc-climbing.org/.
36. Dynamic Pose Tracking Performance Evaluation of HTC Vive Virtual Reality System10.1109/ACCESS.2020.3047698 ↗
37. The Accuracy and Precision of Position and Orientation Tracking in the HTC Vive Virtual Reality System for Scientific Research10.1177/2041669517708205 ↗
38. Borges, M., Symington, A., Coltin, B., Smith, T., and Ventura, R. (2018, January 1–5). HTC Vive: Analysis and Accuracy Improvement. Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.10.1109/IROS.2018.8593707 ↗
39. Astad, M.A., Hauan Arbo, M., Grøtli, E.I., and Tommy Gravdahl, J. (2019, January 6–8). Vive for Robotics: Rapid Robot Cell Calibration. Proceedings of the 7th International Conference on Control, Mechatronics and Automation (ICCMA), Delft, The Netherlands.10.1109/ICCMA46720.2019.8988631 ↗
40. Physical Fitness of Elite Youth Sports Climbers: Gender Differences and Correlates of Climbing Ability10.2478/bhk-2025-0019 ↗
41. Elias, P., Skvarlova, V., and Zezula, P. (2021, January 20). SPEED21: Speed Climbing Motion Dataset. Proceedings of the 4th International Workshop on Multimedia Content Analysis in Sports, Virtual.10.1145/3475722.3482795 ↗
42. Mora-Fernandez, A., Argüello-Arbe, A., Tojeiro-Iglesias, A., Latorre, J.A., Conde-Pipó, J., and Mariscal-Arcas, M. (2024). Nutritional Assessment, Body Composition, and Low Energy Availability in Sport Climbing Athletes of Different Genders and Categories: A Cross-Sectional Study. Nutrients, 16.10.20944/preprints202408.0066.v1 ↗
43. Female Excellence in Rock Climbing Likely Has an Evolutionary Origin10.1016/j.crphys.2021.01.004 ↗