# Assessing Inter-Joint Coordination in CrossFit: A Pilot Study Comparing Advanced and Beginner Practitioners

Océane Dubois, Agnès Roby-Brami, Ross Parry, Nathanaël Jarrassé

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Océane Dubois, Agnès Roby-Brami, Ross Parry, Nathanaël Jarrassé. Assessing Inter-Joint Coordination in CrossFit: A Pilot Study Comparing Advanced and Beginner Practitioners. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the.., 10.46298/mbj.16204. hal-05191757v2

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# Assessing Inter-Joint Coordination in CrossFit: A Pilot Study Comparing Advanced and Beginner Practitioners

Océane Dubois<sup>a*</sup>, Agnès Roby-Brami<sup>a</sup>, Ross Parry<sup>b</sup>, Nathanaël Jarrassé<sup>a</sup>

<sup>a</sup> Sorbonne Université, CNRS, Inserm, Institut des Systèmes Intelligents et de Robotique, ISIR, F-75005 Paris, France

<sup>b</sup> LINP2, UPL, UFR STAPS, Université Paris Nanterre, 200 Avenue de la République, 92001 Nanterre, France

* Corresponding author: dubois@isir.upmc.fr

Received date: 01/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** Movement analysis, Inter-joint coordination

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Kinematic analysis is an accessible method for assessing sports performance, using non-invasive sensors or even just a camera. Various metrics, such as position, velocity, and stride rate, are derived from kinematic data, but most focus on end-effector performance (e.g., hand or foot), while inter-joint coordination is rarely considered.

Inter-joint coordination refers to how joints work together spatially and temporally to produce movement. Evaluating inter-joint coordination proves complex due to inherent difficulties with interpretation of high-dimensional datasets. Commonly used methods, such as Continuous relative phase (CRP, Seifert 2010) or Angle-Angle Plots, also fail to provide comprehensive insights into rehabilitative gains or athletic performance (Dubois 2023).

The human musculoskeletal system offers multiple rotational axes, allowing various joint coordination strategies for the same movement. Assessing inter-joint coordination is essential to understanding how movement patterns arise from redundant degrees of freedom (DoF). For instance, professionals and novices in tennis may have similar hand trajectories but differ in joint coordination, impacting overall movement efficiency. New movement analysis approaches are needed to understand how specific patterns contribute to athletic mastery.

This study uses two new metrics—joint contribution variation based on Principal Component Analysis (JcvPCA) and joint synchronization variation based on

Continuous Relative Phase (JsvCRP)—to assess inter-joint coordination differences between advanced and beginner CrossFit practitioners. Results are correlated with qualitative expert coach evaluations, providing an interpretable framework for analyzing movement coordination in sports performance.

## 2. Methods

### 2.1 Participants and Task

This exploratory study involved two participants of similar age and height. The Advanced participant trained CrossFit three times a week for four years, while the Beginner trained once a week for two years, both injury-free. They performed the snatch, a weightlifting movement where the barbell is lifted from the ground to overhead in one motion. This is considered a one-degree-of-freedom task based on bar height at full arm extension. Performances were filmed from the same viewpoint, and one representative trial per participant was selected.

### 2.3 Data Analysis

Recorded videos were processed to extract skeletal data (Acı 2023), from which 3D joint angles were calculated. A low-pass filter (3 Hz cutoff) was applied to reduce noise. Only right-side joint angles (hip, knee, shoulder, and elbow) were analyzed, assuming symmetry between sides. Data were analyzed using inter-joint coordination metrics specific to discrete movement: JcvPCA (assessing variation in joint contribution between 2 datasets) and
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Comparison of a Beginner and an Advanced Crossfit practitioner. First row: raw joints angles trajectories. Second row left: Difference of joint contribution (JcvPCA) between participants (higher values show higher contribution in beginner). Second row Right Joint synchronization (JsvCRP) higher values show higher differences between participants.</figcaption></figure>

JsvCRP (evaluating variation of temporal synchronization between 2 datasets) (Dubois 2025). JscCRP quantifies differences between two CRP curves using the area between them, whereas JcvPCA assesses joint contribution variation by reprojecting the compared dataset onto the reference dataset. A CrossFit coach also analyzed the videos.

## 3. Results and discussion

Figure 1 (top row) presents joint angle trajectories, though visual differences are difficult to interpret. Therefore, spatial and temporal inter-joint coordination (bottom row) were analyzed. For spatial contribution (JcvPCA), minimal differences were observed at the ankle, knee, hip, and elbow. However, the shoulder ($JcvPCA_{shoulder} = -0.11$) showed reduced involvement in the Beginner compared to the Advanced participant. As the coach noted, “*The advanced individual's wider grip enables better shoulder engagement*” which improves rotator cuff stability during extension.

Temporal synchronization revealed joint desynchronization with higher JsvCRP values. In this case, joints synchronization differs especially between the elbow and lower-body joints, as well as ankle-knee. The coach observed, “*The beginner's elbow pull is too early*” and noted

a distinct “*double ankle extension*,” reflecting an alternative—though not incorrect—strategy. This underscores that the method detects movement differences without prescribing correctness, which may vary with mobility or individual style.

This study has few limitations. First, it is based on a single repetition per participant and a small sample size (N=2), limiting generalizability. Additionally, while the joint angle extraction method based on videos is valuable, its precision may be lower than marker-based systems. However, this portable and non-invasive method remains a practical tool for movement analysis in various training environments.

Test the workflow application: https://tinyurl.com/42rwxcha

## 4. Conclusions

This study introduced a framework for evaluating inter-joint coordination in movement analysis, focusing on the snatch exercise performed by advanced and beginner CrossFit practitioners. Using JcvPCA and JsvCRP metrics, we identified key coordination differences between the two participants, confirmed by a coach's qualitative analysis. These results highlight the potential of this method to reveal coordination
differences difficult to appreciate through traditional kinematic metrics. This framework could be applied across various sports to assess joint coordination in complex movements.

## Acknowledgments

The authors would like to thank Julien Miniou and Come Fradelin for their contribution to the crossfit analysis and movement recordings.

## Conflict of Interest Statement

None.

## Contributor Roles

OD: Conceptualization, Methodology, Writing original draft, NJ: Conceptualization, Funding acquisition, Validation, Supervision, RP: Writing-review & Validation, AR: Writing-review & Validation.

## Funding

This study has been funded by the French National Research Agency with the Exoman Project (19-CE33-0009).

## Data, software, code availability

Provide any hyperlink referring to opensource data, software or code.

## References

Alzahrani, A., & Ullah, A. (2024). Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance. *Digital health, 10*, 20552076241256745. https://doi.org/10.1177/20552076241256745

Seifert, L., Leblanc, H., Chollet, D., & Delignières, D. (2010). Inter-limb coordination in swimming: effect of speed and skill level. *Human movement science, 29*(1), 103–113. https://doi.org/10.1016/j.humov.2009.05.003

Dubois, O., Roby-Brami, A., Parry, R. et al. (2023). A guide to inter-joint coordination characterization for discrete movements: a comparative study. *J NeuroEngineering Rehabil, 20*, 132. https://doi.org/10.1186/s12984-023-01252-2

Acı N., & Kuluöztürk, M. F. (2023). Accuracy Detection in Some Sports Training Using Computer Vision and Deep Learning Techniques. *Bitlis Eren University*

*Journal of Science and Technology, 13*(2), 133–158. https://doi.org/10.17678/beuscitech.1330481

Dubois, O., Roby-Brami, A., Parry, R., & Jarrassé, N. (2025). JcvPCA and JsvCRP: a set of metrics to evaluate changes in joint coordination strategies. *arXiv [Cs. RO]*. Retrieved from http://arxiv.org/abs/2505.09020