# Agreement of Gait Events Detection by Inertial Measurement Units and an open source Markerless End-to-End Workflow?

Ahmed Yacine Lardjane, Mathias Blandeau, Laura Wallard, Christopher Paglia, Christophe Gillet, Sébastien Leteneur, Emilie Simoneau-Buessinger

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Ahmed Yacine Lardjane, Mathias Blandeau, Laura Wallard, Christopher Paglia, Christophe Gillet, et al. Agreement of Gait Events Detection by Inertial Measurement Units and an open source Markerless End-to-End Workflow?. Multidisciplinary Biomechanics Journal, 2025, 49th congress of the Société de Biomécanique, 10.46298/mbj.14470. hal-04737941v2

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# Agreement of Gait Events Detection by Inertial Measurement Units and an open source Markerless End-to-End Workflow

Ahmed Yacine Lardjane<sup>a, b*</sup>, Mathias Blandeau<sup>a</sup>, Laura Wallard<sup>a</sup>, Christopher Paglia<sup>b</sup>, Christophe Gillet<sup>a</sup>, Sébastien Leteneur<sup>a</sup>, Emilie Simoneau-Buessinger<sup>a</sup>

<sup>a</sup> LAMIH, CNRS, UMR 8201, Univ. Polytechnique Hauts-de-France, Valenciennes, France

<sup>b</sup> IRT Railenium, Valenciennes, France

* Corresponding author: AhmedYacine.Lardjane@uphf.fr

Received date: 06/04/2024

Accepted date: 28/06/2024

Publication date: 31/01/2025

**Keywords:** Spatiotemporal, IMU, Markerless, Gait, Pose2Sim

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Precise spatiotemporal calculation and proper analysis of kinematic and kinetic variables during walking depend on gait cycle events, usually detected by force plates. Those variables are widely used by clinicians, but also in urban planning, particularly for pedestrian crossing where walking speed is investigated in different environments, such as roads or railways stations (Grison et al. 2022). Motion capture outside laboratories is challenging, leading to the rise of accelerometer-based Inertial Measurement Units (IMUs) as a popular alternative. Markerless motion analysis technologies have emerged, with numerous commercial systems available. However, these systems can be costly and often rely on proprietary code and datasets for their training (Wade et al. 2022). Pose2Sim represents a recent breakthrough in markerless motion capture. This open source workflow uses 2D pose estimation data from videos, leveraging validated open source pose estimation models such as OpenPose and AlphaPose, as well as custom-trained models through open source tools like DeepLabCut. Pose2Sim identifies and associates individuals, accurately triangulates joint positions, and integrates with an OpenSim model (Pagnon et al. 2022). It supports any type of camera, broadening its applicability across various study settings. Despite extensive research in IMU and kinematics for walking event estimation, literature lacks a comparative analysis between open source, markerless motion capture solutions and gold standards. This study aims to evaluate the agreement

of three open source algorithms for walking events detection from force plates, IMU and videos.

## 2. Methods

### 2.1 Protocol

Four Kistler Type 9286 force plates (500Hz), 4 Delsys Trigno IMUs (370Hz) and 5 Miquis video cameras (25 Hz), mounted on the wall, ensuring a full view of the entire walkway were used in this study. All devices were synchronized by Qualisys software. IMUs were attached to the participants' first metatarsals and the lateral aspects of their heels. The study included one healthy young male (age: 31 yrs, 1.73 m, 71 kg) who walked on a 5-meters corridor equipped with the force platforms, turned around, and returned. A valid trial required a complete foot contact per force plate. The participant kept a forward gaze, avoiding force plate targeting. A total of 125 shoed and 125 barefoot walks were completed. Rest periods were given every 15 trials, and a 5-minutes break after 45 trials.

### 2.2 Data analysis

For both conditions, reference events were identified using force platform data on Matlab, with heel strikes (HS) and toe-offs (TO) when the vertical force exceeded or dropped below 20N, respectively (Blandeau et al. 2023). For the IMU, a first-order low-pass Butterworth filter with a 10Hz cutoff was applied to acceleration data, while a first-order band-pass Butterworth filter, spanning 0.001 to 5Hz was applied to gyroscope data. Then both
acceleration and gyroscope data were smoothed using a Savitzky-Golay filter on the flexion axes. Maximum and minimum points of these filtered signals were identified as HS and TO, respectively (Gottlieb et al. 2020). For videos processing, body's keypoints were identified using the opensource BODY_25B OpenPose model as it is the most accessible pre-trained model (Stenum et al. 2023) and were processed using Pose2Sim end-to-end workflow. Events were then identified as proposed in Zeni, et al. (2008). All codes are open source and available on https://github.com/yacine1007/imu-pose2sim-fp

## 3. Results and discussion

In total, 305 and 350 footstrikes were validated in bare-foot and shoed conditions, respectively. The mean error results are presented in Table 1 and Figure 1.

<table><caption>**Table 1.** Mean error and SD (in seconds) between IMU and video algorithms and force platforms.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Heel Strike</th><th colspan="2">Toe-Off</th></tr><tr><th>IMU</th><th>Video</th><th>IMU</th><th>Video</th></tr></thead><tbody><tr><th rowspan="2">Barefoot</th><td>0.060*</td><td>0.005*</td><td>0.024*</td><td>-0.068*</td></tr><tr><td>±0.020</td><td>±0.022</td><td>±0.010</td><td>±0.047</td></tr><tr><th rowspan="2">Shoed</th><td>0.030*</td><td>-0.006*</td><td>0.028*</td><td>-0.050*</td></tr><tr><td>±0.020*</td><td>±0.018</td><td>±0.006</td><td>±0.050</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Mean errors distributions (in seconds) between algorithms and force platforms.</figcaption></figure>

A Shapiro-wilk test was applied to verify the normality of the data, followed by a non-parametric Friedman

ANOVA in all conditions. For both barefoot and shoed conditions, the applied tests revealed a significant difference between gait event detection for both HS and TO obtained with IMU and video compared to data obtained with force platforms, (p < 0.001). Our results are in line with the studies of Blandeau et al. (2023) and Stenum et al. (2023). In both shoed and bare-foot conditions, IMUs errors were less dispersed than videos' errors (Figure 1), this might be due to optimal IMU placement on the metatarsal and their higher capture rate (Gottlieb et al. 2020). The distribution of video errors was mostly negative, suggesting a detection that occurred after the event happened. This could be attributed to a misdetection by the model and to the low recording rate. Improving the recording rate might enhance the model accuracy.

## 4. Conclusions

This study aimed to evaluate the agreement among open source algorithms using force plate data, video analysis and IMU for gait event detection. All three methods detected walking events (HS and TO). Although errors were detected, results align with previous studies, encouraging the use of open source models. However, it must nevertheless be done carefully. Further investigations should be conducted on larger samples under different conditions.

## Conflict of Interest Statement

The authors declare no conflict of interest.

## References

* Blandeau M, Lesaffre R, Wallard L. 2023. Validation of walking events detecting algorithm during Nordic walking of healthy subjects. *Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization*. https://doi.org/10.1080/10255842.2023.2246304

* Gottlieb U, Balasukumaran T, Hoffman JR, Springer S. 2020. Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units. *Sensors*. https://doi.org/10.3390/s20216331

* Grison E, Aupetit S, Escaich S, Morgagni S. 2022. Using virtual reality to evaluate the effects of two safety systems at pedestrian track crossings on human behaviour.

<footnote>* Represents a significant difference (p<0.05) in gait event detection between IMU or Video and force platforms</footnote>
Pagnon D, Domalain M, Reveret L. 2022. Pose2Sim: An open-source Python package for multiviewmarkerless kinematics. JOSS. https://doi.org/10.21105/joss.04362

Stenum J, Hsu MM, Pantelyat AY, Roemmich RT. 2023. Clinical gait analysis using video-based pose estimation: multiple perspectives, clinical populations, and measuring change https://doi.org/10.1101/2023.01.26.23285007

Wade L, Needham L, McGuigan P, Bilzon J. 2022. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ. https://doi.org/10.7717/peerj.12995

Zeni JA, Richards JG, Higginson Two simple methods for determining gait events during treadmill and over-ground walking using kinematic data. Gait & Posture. https://doi.org/10.1016/j.gaitpost.2007.07.007