# Estimation of squat jump performance parameters using a markerless approach: a pilot study

Brice Guignard, Thomas Dumont, Yoann Blache

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Brice Guignard, Thomas Dumont, Yoann Blache. Estimation of squat jump performance parameters using a markerless approach: a pilot study. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the.., 10.46298/mbj.16159. hal-05191711v2

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Distributed under a Creative Commons Attribution 4.0 International License
# Estimation of squat jump performance parameters using a markerless approach: a pilot study

Brice Guignard*, Thomas Dumont, Yoann Blache

a Université Claude Bernard Lyon 1, LIBM, Laboratoire Interuniversitaire de Biologie de la Motricité, UR 7424, F-69622 Villeurbanne, France

* Corresponding author: brice.guignard@univ-lyon1.fr

Received date: 05/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** vertical jump, optoelectronic systems, computer vision, pose estimation model

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

## 1. Introduction

Vertical jumps (e.g., counter-movement jump, squat jump) are widely used to assess athletic power (Wade et al., 2020). In laboratory settings, motion capture (MoCap) and force plates can provide precise biomechanical analysis but are costly, complex, and time-consuming, limiting their routine use outside the laboratory (Aleksic et al., 2024). Recent advancements in pose estimation algorithms offer a promising alternative, enabling jump analysis with minimal equipment—such as a stabilized smartphone or low-cost camera. Among these, MMPose has emerged as a viable 2D markerless solution to address MoCap limitations (Aleksic et al., 2024). Other tools, including Movenet, OpenPose, HRNet, AlphaPose, MediaPipe, and DeepLabCut, also provide a good potential for estimation of jump biomechanics (Roggio et al., 2024). Specifically, Google AI's MediaPipe provides real-time, high-fidelity body tracking, proving its efficiency for gait analysis and remote fitness monitoring (Roggio et al., 2024). However, its accuracy in assessing jump performance compared to MoCap remains unclear.

This study aimed therefore to evaluate the ability of MediaPipe to accurately estimate jump performance biomechanics parameters.

## 2. Methods

### 2.1. Participants

Seven healthy participants (including 6 men; age: 26.9 ± 6.8 years; height: 179.0 ± 8.02 cm; body mass:

77.7 ± 8.4 kg) signed an informed consent and were included in this study.

### 2.2. Procedure

After a standardized warm-up and a familiarization phase, participants performed a single maximal squat jump (SJ) as follow: descending into a static squat for two seconds with hand palms on the chest, head upright, and gaze forward. Jumps were performed barefoot, wearing minimal, form-fitting clothing.

A sagittal 2D video was recorded (iPad Pro 11", M4 chip, Apple Inc., Cupertino, USA, 1920×1080 resolution, 120 Hz) from the right body side (participants 1, 4, 7) or left body side (participants 2, 3, 5, 6). Additionally, a 3D marker-based system (Qualisys AB, Gothenburg, Sweden, 200 Hz) with 14 Miqus M3 cameras tracked five markers placed on the acromion (Ac), greater trochanter (Tr), lateral femoral condyle (Co), lateral malleolus (Ma), and distal phalanx of the second toe (Ph). Each participant holds a standing upright posture before the jump to set their height in meters, that serves as a scale for pixel to metric conversions.

### 2.3. Data treatment

For each video, MediaPipe body landmarks pose estimator (Bazarevsky et al., 2020) was used to extract the 2D coordinates of the five anatomical landmarks. Similarly, 2D marker trajectories were obtained from QTM software (2024.3). All coordinates were filtered
using a 4th-order low-pass Butterworth filter, with an
8 Hz cutoff frequency. Initiation of the push-off phase
was considered when the vertical position of the shoul-
der was 2 cm higher than its baseline value calculated
during the first 0.5 s of the squat pose. Toe-off and
landing events were automatically detected when the
vertical position of Ph was 5 mm higher than its baseline
value. Flight time (*Flight*<sub>t</sub>) was considered from toe-off
to landing instants and used to compute vertical jump
height (*Jump*<sub>h</sub>): *Jump*<sub>h</sub> = 1/8 × *g* × *Flight*<sub>t</sub><sup>2</sup>.

Anatomical landmarks coordinates and anthropo-
metric table were used to compute the body center
of gravity (CG). Additionally, 2D hip, knee and ankle
angles were computed.

Finally, the estimated jump parameters were com-
pared between the markerless and marker-based
approach throughout the movement. The root mean
squared error (RMSE) was calculated from jump initi-
ation to take-off for all kinematical parameters.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** RMSE values for X and Y coordinates (top panel) and lower limb joint angles (bottom panel). For visual clarity, an Ac outlier on Y axis (152 mm) was excluded from the graph.</figcaption></figure>

Credit: Guignard.

**3. Results and discussion**

The mean ± SD *Flight*<sub>*t*</sub> was 511 ± 76 ms for MoCap and
514 ± 79 ms for the markerless approach, representing
a slight difference of 0.7% on average. Similarly, *Jump*<sub>*h*</sub>
was measured at 32.6 ± 10.0 cm with MoCap and
33.1 ± 10.1 cm with the markerless approach, showing
a minor 1.53% difference between the two methods.

Figure 1 shows the RMSE, with a mean ± SD devia-
tion of 24.26 ± 13.97 mm between the two methods for
all X and Y key point coordinates. These results align with
those reported by Dill et al. (2024), who also analyzed
squat jumps. Deviations in RMSE should be interpreted
in light of MediaPipe's joint position outputs, which do
not perfectly correspond to the anatomical landmarks
defined by the MoCap system (Dill et al., 2024). Future
developments should focus on preprocessing techniques
to enhance comparability between these positions.

Finally, regarding the lower limb joint angles, the
highest mean RMSE was observed for the ankle
(10.85 ± 2.4°), while the lowest was recorded for the
knee (4.78 ± 9.6°; Figure 1). These values remain within
the range reported in a recent study (i.e., 10.7°), which
compared reconstructed 3D knee data from a marker-
less approach to a MoCap reference (Dill et al., 2024).

**4. Conclusions**

This study represents a first step in validating a mark-
erless method for assessing vertical jump performance

against MoCap. It therefore suggests that the markerless
method provides reasonably accurate estimates of key
squat jump performance parameters, such as flight
time and jump height.

Future work will expand to other jump modalities
(e.g., drop jumps) and leverage MediaPipe's versatility
to provide users real-time performance feedback.

**Conflict of Interest Statement**

None.

**Contributor Roles**

BG: Conceptualization, Methodology, Formal analysis, Supervision, Writing original draft, Writing-review & editing; TD: Investigation, Data curation, Writing-review & editing; YB: Conceptualization, Methodology, Software, Validation, Supervision, Writing-review & editing draft.

**Funding**

None.

**Data, software, code availability**

None.

**References**

Aleksic, J., Mesaroš, D., Kanevsky, D., Kneževic, O.M.,
Cabarkapa, D., Faj, L., & Mirkov, D.M. (2024).
Advancing field-based vertical jump analysis: markerless pose estimation vs. force plates. *Life, 14*, 1641.
https://doi.org/doi:10.3390/life14121641

Bazarevsky, I., Grishchenko, K., Raveendran, T.L., Zhu, F., Zhang, Grundmann, M. (2020). Blazepose: on-device real-time body pose tracking. *IEEE Comput Vis Pattern Recognit.* arXiv preprint arXiv:2006.10204.
https://doi.org/10.48550/arXiv.2006.10204

Dill, S., Ahmadi, A., Grimmer, M., Haufe, D., Rohr, M., Zhao, Y., Sharbafi, M., & Hoog Antink, C. (2024). Accuracy evaluation of 3D pose reconstruction algorithms through stereo camera information fusion for physical exercises with MediaPipe Pose. *Sensors, 24*, 7772.
https://doi.org/10.3390/s24237772

Roggio, F., Trovato, B., Sortino, M., & Musumeci, G. (2024). A comprehensive analysis of the machine learning pose estimation models used in human movement and posture analyses: A narrative review. *Heliyon, 10*, e39977. https://doi.org/10.1016/j.heliyon.2024.e39977

Wade, L., Lichtwark, G. A., & Farris, D. J. (2020). Comparisons of laboratory-based methods to calculate jump height and improvements to the field-based flight-time method. *Scand J Med Sci Sports, 30*, 1–37. https://doi.org/10.1111/sms.13556