# A new methodological approach for analyzing bouldering fall kinematics: preliminary study on 6 athletes

Nathan Carretier, Erwan Beurienne, Marie-Hélène Beauséjour Claire

Bruna-Rosso, Marine Dorsemaine, Michel Behr, Nicolas Bailly, Julien Clément

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Nathan Carretier, Erwan Beurienne, Marie-Hélène Beauséjour Claire Bruna-Rosso, Marine Dorsemaine, Michel Behr, et al.. A new methodological approach for analyzing bouldering fall kinematics: preliminary study on 6 athletes. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the., 10.46298/mbj.16165 . hal-05191717v2

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# A new methodological approach for analyzing bouldering fall kinematics: preliminary study on 6 athletes

Nathan Carretier<sup>a</sup>, Erwan Beurienne<sup>b, c</sup>, Marie-Hélène Beauséjour<sup>a</sup>, Claire Bruna-Rosso<sup>b</sup>, Marine Dorsemaine<sup>b</sup>, Michel Behr<sup>b</sup>, Nicolas Bailly<sup>b</sup>, Julien Clément<sup>a, d*</sup>

a École de Technologie Supérieure, Montréal, Canada

b Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France

c Tyyny, Context', Le Chambon Feugerolles, France

d Institut national du sport du Québec, Montréal, Canada

* Corresponding author: julien.clement@etsmtl.ca

Received date: 06/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** bouldering, fall, inertial measurement unit, markerless, videos analysis

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Bouldering, a discipline of sports climbing, is performed without belaying systems, exposing climbers to falls of 3 to 5 meters, potentially causing lower-limb injuries (Auer et al., 2021). Müller et al. (2022) observed a linear increase in bouldering-related injuries at Munich's University Hospital between 2010 and 2020, correlating with the rise in climbers.

Traditional marker-based methods are impractical in climbing gyms and disruptive to athletes. Recent studies have explored video-based 3D motion capture with personalized avatars for performance analysis in speed climbing (Reveret et al., 2020), but these methods require laboratory calibration and fail to capture real-life fall dynamics. This work aims to introduce a markerless multi-camera setup using the library Pose2Sim which has been validated in walking, running, and cycling (Pagnon et al., 2022) to capture joint angles and body positions in bouldering environments.

## 2. Methods

### 2.1. Equipment Setup and Calibration

This study was approved by École de technologie supérieure's ethical committee (H20240603). It used markerless motion capture system with five GoPro (HERO12 Black, GoPro, inc., USA) cameras, capturing at 2.7K and 240 fps which were calibrated using an A3 checkerboard (intrinsic calibration) and a 3D calibration cube (extrinsic calibration) following Pose2Sim

workflow. Blue Trident IMU (Vicon Motion Systems, UK, 1600 Hz, +/-200g) were placed on the ankle, thigh, sacrum, and forehead, and calibrated through a standardized dynamic procedure involving figure-eight movements.

### 2.2. Acquisition and Kinematic Analysis of Falls

Eight falls from 6 different athletes (4 girls and 2 boys) were collected during multiple recording sessions in two distinct indoor climbing gyms. The athletes were equipped with IMUs and recorded during their regular training. No specific instructions were given to the climbers in order to preserve natural behavior. Reference events (T-pose, jump, hand clap) enabled alignment of data sources.

### 2.3. Data Processing

To adapt filtering to the high-impact, non-cyclic dynamics of bouldering falls, we used Yu's method (Winter, 2009) to determine optimal cut-off frequencies. Across all falls, these frequencies showed minimal variability: 13.16–13.19 Hz for Pose2Sim position data and 39.33–39.97 Hz for IMU accelerations. To standardize processing, the lowest values rounded down (13 Hz for Pose2Sim, 39 Hz for IMUs) were applied globally using a 4th-order Butterworth low-pass filter. Pose2Sim signals were filtered per axis, then double-differentiated before computing the norm while the IMUs data were filtered per axis prior to norm calculation.
## 3. Results and discussion

The analyzed falls occurred from various heights (1m to 4m), with different reception postures: feet only (1), feet then hands (2), feet then hands then back (3), feet then back (2). This variability explains the range of peak accelerations.

Differences appear between the IMUs and Pose2Sim methods, especially at the hip (43.23±12.74%), but also at the ankle (37.7±16.48%) and at the head (11.66±13.13%).

<table><caption>**Table 1.** Comparison of peak acceleration norm (m/s²) measurements (IMUs vs Pose2Sim).</caption><thead><tr><th rowspan="2"></th><th colspan="2">Ankle</th><th colspan="2">Hip</th><th colspan="2">Head</th></tr><tr><th>IMU</th><th>P2S</th><th>IMU</th><th>P2S</th><th>IMU</th><th>P2S</th></tr></thead><tbody><tr><th>Mean (m/s²)</th><td>205.5<br>±60.3</td><td>128.1<br>±34.7</td><td>110.8<br>±17</td><td>62.9<br>±13.1</td><td>50.2<br>±13.6</td><td>44.3<br>±14.6</td></tr><tr><th>Error (%)</th><td colspan="2">37.7±16.48</td><td colspan="2">43.23±12.74</td><td colspan="2">11.66±13.13</td></tr><tr><th>t-statistic</th><td colspan="2">3.37</td><td colspan="2">8.04</td><td colspan="2">2.50</td></tr><tr><th>p-value</th><td colspan="2">0.012</td><td colspan="2">0.00009</td><td colspan="2">0.041</td></tr></tbody></table>

As shown in Figure 1, IMUs report the highest peak accelerations at the ankle, followed by the hip and head, reflecting the typical biomechanical sequence during vertical impacts. Pose2Sim estimations follow the same trend but report lower peak values and statistical analysis confirms these differences are significant for all segments. The paired t-test revealed a statistically significant difference between IMU and Pose2Sim measurements at the ankle (t = 3.37, p = 0.012), hip (t = 8.04, p < 0.001), and head (t = 2.50, p = 0.041). These results indicate that, despite similar trends, the differences are not attributable to chance, which highlights the limitations of markerless estimation based on double differentiation during high-frequency impacts.

Overall, while Pose2Sim captures the general trend and relative ranking of accelerations across body segments, it does not yet provide accurate peak estimates to replace IMUs particularly for high-impact segments like the ankle. Discrepancies likely result from segment occlusion (e.g., ankle hidden by the crash pad), pose estimation errors (e.g., keypoint dropouts or swaps), and a lower video sampling rate (240 Hz vs. 1600 Hz for IMUs).

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Distribution of peak acceleration norm for IMUs and Pose2Sim across different sensors (Ankle, Hip, Head).</figcaption></figure>

## 4. Conclusions

Pose2Sim has demonstrated strong accuracy in controlled, low-impact activities such as walking, running, and cycling (Pagnon et al., 2022), but the present results show that its application to the high-impact, rapid dynamics of bouldering falls remains limited.

Future studies will focus on the analysis of bouldering fall kinematics using IMUs to quantify peak accelerations, and Pose2Sim to gather information on body positioning and segment trajectories during the fall.

## Acknowledgements

We thank Horizon Roc, the Fédération québécoise de la montagne et de l'escalade (FQME) and all participating climbers.

## Conflict of Interest Statement

The authors have no conflict of interest to report.

## Contributor Roles

NC: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft; EB: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – review & editing; MHB: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing – review & editing; CBR: Supervision, Validation; MD: Supervision, Validation; MB: Supervision, Validation; NB: Supervision, Validation; JC: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing – review & editing.
## Funding

This study was funded by École de technologie supérieure and Mitacs (IT44490).

## References

Auer, J., Schöffl, V. R., Achenbach, L., Meffert, R. H., & Fehske, K. (2021). Indoor bouldering—A prospective injury evaluation. *Wilderness & Environmental Medicine, **32**(3), 293–301. https://doi.org/10.1016/j.wem.2021.02.002

Müller, M., Heck, J., Pflüger, P., Greve, F., Biberthaler, P., & Crönlein, M. (2022). Characteristics of bouldering injuries based on 430 patients presented to an urban emergency department. *Injury, **53*(6), 1394–1400. https://doi.org/10.1016/j.injury.2022.02.003

Reveret, L., Chapelle, S., Quaine, F., & Legreneur, P. (2020). 3D Visualization of body motion in speed climbing. *Frontiers in Psychology, **11*, 2188. https://doi.org/10.3389/fpsyg.2020.02188

Pagnon, D., Domalain, M., & Reveret, L. (2022). Pose2Sim: An end-to-end workflow for 3D markerless sports kinematics—Part 2: Accuracy. *Sensors, **22*(7), 2712. https://doi.org/10.3390/s22072712

Winter, D. A. (2009). *Biomechanics and motor control of human movement* (4th ed.). Hoboken, NJ: Wiley.