<figure><img src="image_1.png" /></figure>

HAL
open science

# Marker less motion capture accuracy in children with cerebral palsy and typically developing children.

Alexandre Naaïm, Julie Rozaire, Lucas Mercier, Mickaël Begon, Yosra Cherni

<figure><img src="image_2.png" /></figure>

## To cite this version:

Alexandre Naaïm, Julie Rozaire, Lucas Mercier, Mickaël Begon, Yosra Cherni. Marker less motion capture accuracy in children with cerebral palsy and typically developing children.. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the.., 10.46298/mbj.16215 . hal-05191769v2

HAL Id: hal-05191769

https://hal.science/hal-05191769v2

Submitted on 23 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Markerless motion capture accuracy in children with cerebral palsy and typically developing children

Alexandre Naaïm<sup>a*</sup>, Julie Rozaire<sup>a, b</sup>, Lucas Mercier<sup>c</sup>, Mickaël Begon<sup>d</sup>, Yosra Cherni<sup>d, e</sup>

<sup>a</sup> Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T9406, Lyon, France

<sup>b</sup> Centre Médico-Chirurgical de Réadaptation des Massues Croix-Rouge française, Lyon, France

<sup>c</sup> Laboratoire d'Innovation ouverte - École de technologie supérieure, Montréal, Canada

<sup>d</sup> S2M lab, Université de Montréal, Montréal (Qc), Canada

<sup>e</sup> NNL Lab, Centre de recherche Azrieli du CHU Sainte Justine

* Corresponding author: alexandre.naaim@univ-lyon1.fr

Received date: 06/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** motion analysis, markerless, upper limb, pediatrics, cerebral palsy

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Accurate assessment of upper limb function is essential for treatment planning, intervention assessment, and monitoring clinical progression in pediatric rehabilitation. Markerless motion capture using pose estimation with RGB cameras is gaining popularity in the clinical context due to its ease of use, cost-effectiveness, and minimal burden on participant. Although previous studies have primarily validated these markerless systems for lower limb movements (Kanko, Laende, Davis, Selbie, & Deluzio, 2021), their accuracy for upper limb assessment, especially in pediatric populations, remains relatively unexplored. This gap is especially pronounced for children with neuromotor impairments, such as cerebral palsy (CP). Since these models are trained on a wide range of individuals and are solely designed to estimate pose, the appearance of any joint - whether from a child with CP or TD, should not differ significantly. Therefore, no difference in model accuracy is expected. However, given that the networks are trained exclusively on asymptomatic populations, the question could still be raised. As a result, this study aimed to quantify differences in joint location accuracy between an RGB-based markerless system and a marker-based reference method in typically developing (TD) children and children with CP.

## 2. Methods

### 2.1 Measurement

Five TD children (7±3 years, 24±5 kg, 124±14 cm) and five children with CP (5±2 years, 24±10 kg, 118±20 cm) were recruited. Children with CP were MACS levels I or II (Manual Ability Classification System) which corresponds to moderate impairment. All children performed two categories of upper-limb tasks: *capacity* tasks (SAA: Shoulder Abduction-Adduction, SFE: Shoulder Flexion-Extension, EPS: Elbow Pronation-Supination, EFE: Elbow Flexion-Extension) and *performance* tasks (Hat: putting on a hat, R&M: Reaching and Manipulating, OBP: Opening a Bottle and Pouring, Cym: using Cymbals) as defined in the International Classification of Functioning, disability and health (World Health Organization, 2001). Capacity tasks reflect what a participant is physically able to perform, whereas performance tasks represent how movements are executed in daily-life activities.

Kinematics was recorded using 8 RGB cameras Prime Color and 10 optoelectronic cameras Prime 13 from Optitrack (NaturalPoint, Inc., Corvallis, United States), sampled at 60 Hz and 120 Hz respectively. Reflective markers were placed on anatomical landmarks following ISB recommendation. The glenohumeral joint center was estimated using the regression method of Rab et al. (Rab, Petuskey, & Bagley, 2002) The elbow,
<figure><img src="image_4.png" /><figcaption>**Figure 1.** 3D difference between joint centers for the markerless and marker-based for CP (in purple) and TD (in green) for the different task.</figcaption></figure>

wrist, and hand centers were defined as the midpoints of the medial and lateral epicondyles, the ulnar and radial styloid processes, and the heads of the second and fifth metacarpal bones, respectively.

## 2.2 Data processing

Pose estimation on RGB video was performed using the RTMPose (Jiang et al., 2023) model. This model was chosen from a pre-study comparing different available pose detection model as the most accurate. Detected keypoints included the shoulder, elbow, and wrist centers, as well as two hand points approximating the second and fifth metacarpal heads. A weighted 3D triangulation using a direct linear transform weighted from the confidence from the heatmaps for each keypoint, ignoring data with a confidence below 0.5 was applied.

The 3D distance between joint positions from the optoelectronic system and the RGB camera was computed. Boxplots representing the distribution of this distance were plotted for each task, joint, and population. For each joint, we computed the median difference between the two populations for every task and averaged those medians to get a single summary value. For TD children, left and right joints were grouped together whereas for CP children only their affected side was considered.

## 3. Results and discussion

Overall, the magnitude of the accuracy does not differ substantially different between CP and TD groups across the evaluated joints and across capacity and performance tasks. Both the median and the distribution of
the difference are similar suggesting that the markerless pose estimation is not significantly affected. The highest mean median difference was found at the elbow with a value of 5 mm. Median positional difference were generally below 30 mm for the shoulder and approximately 20 mm or less for elbow, wrist, and hand across all tasks. These results are comparable to those reported by Lahkar et al. (2022), who found errors of 23, 31, and 18 mm at the shoulder, elbow, and wrist, respectively. The highest errors for both population were found at the shoulder joint during tasks involving significant shoulder flexion-extension or abduction-adduction as seen on the Figure 1 on the task SAA, SFE and Hat. This difference might be due to the inaccuracy of the keypoints' detection and to the method for location the shoulder joint center using markers.

## 4. Conclusions

Our preliminary results suggest that markerless systems perform similarly across different task types in both populations. These results align with our initial expectations. As similar levels of error were observed in both groups, future validation studies of measurement precision may be conducted on non-pathological populations. Studies involving pathological populations should instead focus on clinically meaningful outcome measures.

## Acknowledgements

All authors are members of the LIA EVASYM.

## Conflict of Interest Statement

None

## Contributor Roles

AN: Conceptualization, data curation, formal analysis, Software, Investigation, Writing original draft; JR: Conceptualization, Data curation, Investigation; LM: Writing-review & editing, Software; MB: Supervision, Writing-review & editing; YC: Supervision, Investigation, Writing-review & editing.

## Funding

This work was partly funded by a grant of the Société de Biomécanique.

## References

Jiang, T., Lu, P., Zhang, L., Ma, N., Han, R., Lyu, C., ... Chen, K. (2023, July 3). *RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose*. arXiv. https://doi.org/10.48550/arXiv.2303.07399

Kanko, R. M., Laende, E. K., Davis, E. M., Selbie, W. S., & Deluzio, K. J. (2021). Concurrent assessment of gait kinematics using marker-based and markerless motion capture. *Journal of Biomechanics*, 127, 110665. https://doi.org/10.1016/j.jbiomech.2021.110665

Lahkar, B. K., Muller, A., Dumas, R., Reveret, L., & Robert, T. (2022). Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing. *Frontiers in Sports and Active Living*, 4. https://doi.org/10.3389/fspor.2022.939980

Rab, G., Petuskey, K., & Bagley, A. (2002). A method for determination of upper extremity kinematics. *Gait & Posture*, 15(2), 113–119.

World Health Organization. (2001). *International classification of functioning, disability and health: ICF*. World Health Organization. Retrieved from https://iris.who.int/handle/10665/42407