# Markerless motion capture for biomechanical analysis of running: influence of number and configuration of cameras

Rinson Remy, Hardouin Emma, Karamanoukian Alexandre, Vignais Nicolas

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Rinson Remy, Hardouin Emma, Karamanoukian Alexandre, Vignais Nicolas. Markerless motion capture for biomechanical analysis of running: influence of number and configuration of cameras. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the.., pp.513-515. 10.46298/mbj.16200. hal-05191753v2

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Submitted on 23 Oct 2025

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# Markerless motion capture for biomechanical analysis of running: influence of number and configuration of cameras

Remy Roinson<sup>a, b*</sup>, Emma Hardouin<sup>a</sup>,
Alexandre Karamanoukian<sup>b</sup>, Nicolas Vignais<sup>a</sup>

<sup>a</sup> Univ. Rennes, INRIA, M2S, F-35000, Rennes, France

<sup>b</sup> Phyling (Orsay, France)

* Corresponding author: remy.roinson@etudiant.univ-rennes2.fr

Received date: 06/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

Keywords: markerless, kinematics, running

© 2025 The Authors
Licence CC-BY 4.0
Published by Société de Biomécanique

## 1. Introduction

Running technique is an important factor for injury prevention and performance, making accurate kinematic analysis a critical step for trainers, clinicians, and researchers. While marker-based motion capture (i.e., optoelectronic systems) remains the most used approach, markerless methods using video and computer vision algorithms are gaining interest due to their lower costs and their capacity for *in situ* analysis (Uhlrich et al., 2023). Various markerless solutions are accessible nowadays, facilitating the measurement of running kinematics. For example, some authors compared joint angle values computed via custom-trained (DeepLabCut) and pre-trained (OpenPose) computer vision techniques during a treadmill running (Van Hooren et al., 2023). Recently, a compiled workflow (Pose2Sim, Pagnon et al., 2022) was developed, enabling 3D pose estimation and full-body joint angle prediction through inverse kinematics (OpenSim, Delp et al., 2007).

Nevertheless, very few studies focused on the setup methodology. Some authors investigated the robustness of camera reduction in markerless method (Muller et al., 2024), showing that reducing the number of cameras can impair accuracy, depending on the configuration. Therefore, the objective of this study was to assess the impact of camera number and placement on the accuracy of lower-limb kinematics during running, providing valuable insights for optimizing the configuration of in situ markerless systems, particularly for running applications.

## 2. Methods

### 2.1 Subjects

Three participants participated to this study (2 males and 1 female; mean ± SD: age, 23±2.1 years, body weight, 66.7±11 kg). The only exclusion criterion was any medical pathology or contraindication to physical activity. Before participation, all subjects received written informed consent detailing the experimental procedures.

### 2.2 Data Collection

A 23-camera marker-based motion capture system (Oqus, Qualisys, Sweden) sampled at 120 Hz was used to obtain reference kinematic data. Markerless kinematic data were computed with a 9-camera markerless motion capture system (Miqus, Qualisys, Sweden) sampled at 60 Hz. Videos cameras were strategically placed with “corner”, “sagittal” and “transversal” cams (see Fig.1). Both systems were synchronized. Opensim software was used to solve inverse kinematic process according to marker-based motion data. For the markerless analysis, the Pose2Sim workflow (Pagnon et al., 2022) was employed to compute pose estimation from videos cameras and inverse kinematics was done through Opensim software. More precisely, the RTMLib computer vision method was used with the HALPE_26 model. For the marker-based method, an adapted Rajagopal model was used.

Participants were asked to run on a 10-meter-long recording area at three different self-selected paces:
'slow,' 'comfortable,' and 'fast.' Each participant completed 10 trials at each velocity condition.

## 2.3 Data analysis

Data analysis was performed using Python (version 3.9). Lower-body joint angles from marker-based and markerless systems were compared for each running cycle and time-normalized to 0–100%. The beginning of the running cycle was defined as the lowest position of the heel landmark. Eight conditions differing by number and placement of cameras were tested: two with four cameras, two with five, two with six, one with eight, and one with nine.

Root-Mean-Squared-Error (RMSE) analysis was performed to compare joint angles extracted from reference and markerless systems across all conditions (see Table 1). This study focused on 3D lower limb joint angles in sagittal plane only.

## 3. Results and discussion

The average RMSE results for lower limbs joint angles (averaged for right and left sides) are presented in Table 1 in sagittal plane. Each tested condition represents a predefined number and placement of cameras used for processing.

<table><caption>**Table 1.** Mean and standard deviation RMSE results for lower limbs joint angles in sagittal plane for each condition tested.</caption><thead><tr><th></th><th>Cams</th><th>Hip</th><th>Knee</th><th>Ankle</th></tr></thead><tbody><tr><td>1</td><td>B/C/G/H</td><td>10.4 ± 4.2</td><td>9.1 ± 1.9</td><td>19.5 ± 7.0</td></tr><tr><td>2</td><td>C/D/F/G</td><td>8.8 ± 1.9</td><td>6.4 ± 2.8</td><td>9.6 ± 31.2</td></tr><tr><td>3</td><td>B/C/D/E/F</td><td>8.7 ± 2.0</td><td>6.4 ± 2.9</td><td>6.1 ± 2.4</td></tr><tr><td>4</td><td>C/D/E/F/G</td><td>8.7 ± 2.0</td><td>6.4 ± 2.9</td><td>6.1 ± 2.4</td></tr><tr><td>5</td><td>B/C/D/F/G/H</td><td>8.4 ± 1.9</td><td>6.1 ± 2.1</td><td>6.7 ± 2.7</td></tr><tr><td>6</td><td>A/B/C/G/H/I</td><td>9.8 ± 3.9</td><td>8.4 ± 1.8</td><td>16.2 ± 6.5</td></tr><tr><td>7</td><td>A/B/C/D/F/G/H/I</td><td>8.5 ± 1.9</td><td>6.3 ± 1.9</td><td>6.9 ± 2.9</td></tr><tr><td>8</td><td>A/B/C/D/E/F/G/H/I</td><td>8.5 ± 1.7</td><td>6.0 ± 2.2</td><td>6.2 ± 2.4</td></tr></tbody></table>

According to the results, the number of cameras did not directly impact accuracy. Notably, the six-camera configuration (Cond.6) resulted in a higher RMSE than both the four-camera (Cond.2) and eight-camera (Cond.7)

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Experimental set-up of video camera placement.</figcaption></figure>

setups. However, camera configuration had a greater influence on pose estimation accuracy. Specifically, when comparing setups with (Cond.5) and without (Cond.6) cameras in the sagittal plane, joint angles were computed more accurately in the setup that included sagittal plane cameras. Thus sagittal plane acquisition appears critical for the computation of lower limb joint angles using markerless system, especially for the ankle.

Our results are consistent with previous studies, which reported similar ranges of lower limb joint angles in the sagittal plane when comparing optoelectronic systems with computer-vision techniques (Pagnon et al., 2022; Van Hooren et al., 2023).

## 4. Conclusions

The number of cameras associated with the placement directly impacts accuracy of lower limb kinematics. Cameras placed to record the sagittal plane of the running movement appear crucial to improve kinematic accuracy.

### Contributor Roles

RR: Formal analysis, Visualization, Methodology, Writing original draft; EH: Methodology, Writing and review; AK: Conceptualization, Supervision, review; NV: Methodology, Supervision, review.

### References

Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., & John, C. T. (2007). OpenSim: Open-source software to create and analyze dynamic simulations of movement. *IEEE Transactions on Biomedical Engineering, 54*(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024
Muller, A., Boulaire, B., Chaumeil, A., Hagemeister, N., Dumas, R., & Robert, T. (2024). Comparison of marker-based and markerless motion analysis: robustness to camera reduction. *Multidisciplinary Biomechanics Journal, 1*, 95–97. https://doi.org/10.46298/mbj.14481ï

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

Uhlrich, S. D., Falisse, A., Kidziński, Ł., Muccini, J., Ko, M., Chaudhari, A. S., ... Delp, S. L. (2023). OpenCap: Human movement dynamics from smartphone videos. *PLoS Computational Biology, 19*. https://doi.org/10.1371/journal.pcbi.1011462

Van Hooren, B., Pecasse, N., Meijer, K., & Essers, J. M. N. (2023). The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics. *Scandinavian Journal of Medicine and Science in Sports, 33*(6), 966–978. https://doi.org/10.1111/sms.14319