# Development and laboratory evaluation of a markerless method to estimate low back intersegmental moments in childcare-related activities

Diana Pardo Ramos, Léan Mazier-Vallet, Adriana Savescu, Raphaël Dumas, Antoine Muller

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Diana Pardo Ramos, Léan Mazier-Vallet, Adriana Savescu, Raphaël Dumas, Antoine Muller. Development and laboratory evaluation of a markerless method to estimate low back intersegmental moments in childcare-related activities. Journal of Biomechanics, 2026, 194, pp.113070. 10.1016/j.jbiomech.2025.113070. hal-05379764

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# Development and laboratory evaluation of a markerless method to estimate low back intersegmental moments in childcare-related activities

Diana Pardo Ramos<sup>a,b</sup>, Léan Mazier-Vallet<sup>a</sup>, Adriana Savescu<sup>c</sup>, Raphaël Dumas<sup>a</sup>, Antoine Muller<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Biomechanics

Ergonomics

Inverse dynamics

Bottom-up and top-down methods

Theia3D

Manual material handling

In situ measurement

## ABSTRACT

In early childhood care settings, the frequent adoption of awkward postures and repetitive manual material handling tasks are biomechanical risk factors for the development of musculoskeletal disorders. Estimating low back intersegmental moments under realistic conditions could help identify such risks. Realistic conditions make measurement challenging, particularly in achieving a balance between unobtrusive instrumentation and acceptable accuracy. The aim of this study was to develop and evaluate a markerless method for estimating low back intersegmental moments for childcare-related activities. In standardized laboratory conditions, eight participants performed six manual material handling and sit-to-stand tasks, representative of real work situations, using two different baby dummies weighting 2.7 kg and 10 kg. The proposed method, only based on video recordings, was evaluated by comparison with a reference approach combining marker-based motion capture and force platforms. Mean absolute differences in L5/S1 joint moment estimation between both approaches were approximately 20 Nm, which is comparable to errors reported in the literature for methods without force platforms that can be deployed in situ. By requiring no instrumentation or participant preparation, this method offers a practical solution for quantifying low back intersegmental moments.

## 1. Introduction

Childcare workers experience a high incidence of musculoskeletal disorders (MSDs) (Bright and Calabro, 1999), with low back pain being especially prevalent (Rossi et al., 2013). In France, 89 % of occupational diseases recognized in this sector involve periarticular conditions (Assurance Maladie and - Risques professionnels, 2023). One contributing factor to these MSDs is the physical demands of childcare, which often include lifting children in postures involving forward trunk flexion of 30° or more, as well as kneeling or squatting (Holtermann et al., 2020; Labaj et al., 2016). The frequent adoption of these awkward postures and repetitive manual material handling (MMH) tasks has been identified as a key biomechanical risk factor, particularly for the lower back (Bright and Calabro, 1999; Gratz et al., 2002). Epidemiological studies in other occupational settings have demonstrated a strong causal relationship between lifting from a standing posture and lower back MSDs (Putz-Anderson et al., 1997), supporting the relevance of these biomechanical factors.

To better understand the mechanical loads associated with MMH, intersegmental moments are considered as an indicator of discomfort and low back pain (Gallagher, 2005; Marras et al., 1993) and are commonly expressed in the literature as a factor of low back pain and MSD (Gallagher et al., 1994; Lavender et al., 2003; Muller et al., 2022, 2020; Plamondon et al., 2010; van der Have et al., 2019; von Arx et al., 2021). The most commonly used method for estimating intersegmental moments employs an optoelectronic system combined with force platforms (FP). The optoelectronic system enables the measurement of the participant's movements and requires the placement of markers on anatomical landmarks. Force platforms measure ground reaction forces and necessitate equipping the laboratory accordingly. The operational constraints of these instruments limit their usability by imposing a preparation phase for the subject. Moreover, the presence of markers during the analysis, as well as the requirement to remain on the force platforms, restricts natural movement. To enhance the ecological validity of such analyses, the development of methods that offer a good balance between unobtrusive instrumentation and acceptable accuracy

<footnote><sup>a</sup> Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T.9406, F-69622 Lyon, France</footnote>

<footnote><sup>b</sup> Conseil Départemental du Val de Marne, Direction des Crèches, 94000 Créteil, France</footnote>

<footnote><sup>c</sup> INRS, Conception - Equipements de protection - Interfaces homme-machine, Nancy, France</footnote>

<footnote>* Corresponding author.
E-mail address: antoine.muller@univ-lyon1.fr (A. Muller).</footnote>
is required.

Several methods have been proposed for in situ MMH analysis. For example, Labaj et al. (2016, 2019) estimated low back forces and moments by combining video recordings with the 3D Static Strength Prediction Program (3D-SSPP), requiring manual placement of a digital avatar on captured images. While relevant, this approach is time-consuming, operator-dependent, and its results depend on the positioning of participants in relation to the camera. Marras et al. (2010) developed a custom automated instrumentation for dynamic spinal load moment estimation. More recently, developments in ambulatory motion capture, especially using inertial measurement unit (IMU), have enabled in situ estimation of intersegment moments (Faber et al., 2016; Muller et al., 2020; Skals et al., 2021, Skals et al., 2017). These methods estimate external forces through measured kinematics to compute intersegmental moments. However, they require the subject to be instrumented, which limits their practicality and acceptance in real occupational settings.

Advances in computer vision have led to the emergence of markerless motion capture techniques based solely on video data (Colyer et al., 2018; Wade et al., 2022). These approaches allow for full-body kinematic estimation without requiring any instrumentation on the participant being analyzed. Several studies have evaluated the accuracy of such methods by comparing the kinematics (Chaumeil et al., 2024; Kanko et al., 2021; Lahkar et al., 2022b; Ripic et al., 2023a; Vafadar et al., 2022) or kinetics (Kanko et al., 2024; Ripic et al., 2023b)

variables to optoelectronic systems. However, estimating intersegmental moments requires knowledge of ground reaction forces with FP. The use of a video-based markerless approach combined with a kinematics-based method for estimating external forces is a promising approach. This would allow the estimation of intersegmental moments solely from video recordings, without requiring physical or invasive modifications to the work environment.

The objective of this study was to develop and evaluate a markerless method for estimating low back intersegmental moments in childcare-related activities. This approach enhances the ecological validity of the analysis by reducing experimental constraints, as it relies solely on video-based measurements. The proposed approach was evaluated in laboratory conditions against a reference method combining optoelectronic motion capture and force platforms.

## 2. Material and methods

### 2.1. Participants

Eight healthy voluntary participants (one man and seven women, BMI = 24.6 ± 3.8 kg/m², age = 28 ± 6 years) took part in the experimental protocol. None of the participants were early childhood professionals. Prior to the experiment they signed an informed consent form. The experimental protocol was approved by the ethical committee of [University Gustave Eiffel, France].

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Illustration of the three different movements performed for the MMH task. Each movement corresponded to a posture in which the baby dummy was lifted from the floor. All three movements were performed with both baby dummies.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Illustration of the three different movements performed for the STS task. Each movement corresponded to the posture from which the baby dummy was transferred. All three movements were performed without load and with both baby dummies.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Illustration of the camera layout.</figcaption></figure>
## 2.2. Tasks

The tasks described below were designed to be representative of typical baby-handling activities in early childhood settings. They were identified through observational studies of work practices (Pardo Ramos, 2025), which included 135 h of observations across five child-care facilities and 69 early childhood professionals. Each participant performed 6 different movements, corresponding to two main tasks, each realized with three different postures (Fig. 1 and Fig. 2). The first main task, named manual material handling (MMH), consisted of lifting and deposing a baby dummy (see details below) from the floor to one of three postures. Participants were instructed to grasp the dummy with both hands positioned symmetrically under its armpits, bring it straight toward their chest, hold the position for a few seconds, and then return the dummy to its original position. The three postures for MMH were standing, squatting, and kneeling. The second main task, named sit-to-stand (STS), consisted in transferring from the postures to a standing pose and returning to the original squatting, kneeling, or sitting posture. Participants were instructed to grasp the dummy as illustrated in Fig. 2 and to keep this grasping throughout the transfer. The three postures for STS were squatting, kneeling, and sitting.

Two baby dummies (2.7 kg weight – 50 cm height and 10 kg weight – 76 cm height) were used to represent a newborn and a one-year-old child, respectively. The three movements of MMH were performed with each of the two baby dummies. The three movements of STS were performed without load and with each of the two baby dummies. The three conditions were named 0 kg, 2.7 kg, and 10 kg.

For each combinations of movement – load, a trial consisted of 3 successive repetitions. To avoid participant fatigue, a minimum of one minute of rest was given between each trial. The order of the trials was randomized for each participant to prevent bias in lifting performance due to fatigue, task difficulty, and learning effects. Before each trial, the experimenter repeated the specific instructions for the movement to be performed, showed pictures of the postures (like those shown in Figs. 1 and 2), and demonstrated how to perform the task from these postures. Then, the participant could practice performing the task. This training period lasted as long as the participant wished.

Participants were required to keep each foot (and, in the case of the kneeling posture, the knee) in contact with one of the force platforms. No additional instructions were provided on how to perform the tasks, so participants were free to choose their own strategy. The only specific instruction concerned the STS – kneeling movement, during which participants had to pass through the lunge-like posture (similar to a knight kneeling, see Fig. 2).

## 2.3. Measurements

Twenty cameras were used in this experimental protocol: 10 optoelectronic cameras (Qualisys Miqus M3, 250 Hz) and 10 video cameras (Qualisys Miqus video, 50 Hz). The two camera systems were paired and evenly placed around the acquisition area (Fig. 3). Ground reaction forces were measured using two synchronized force platforms (Tread-metrix, 2000 Hz). All systems were synchronized and calibrated using the Qualisys calibration kit. Participants were equipped with 48 reflective markers positioned in the whole body as described in (Lahkar et al., 2022a).

## 2.4. Trials segmentation

Trials were manually segmented by repetitions in order to include only the phases where the estimation of intersegmental L5/S1 joint moments was possible. The static phases at the beginning and end of the trials and between repetitions were excluded. Thus, for MMH tasks, each repetition was segmented into a lift phase and a deposit phase (Fig. 1). Similarly, for STS tasks, each repetition was segmented into a stand phase and a return phase (Fig. 2). The beginning of each phase was

identified as the moment when the participant moved from his static posture, and the end was identified as the moment when the participant returned to a static posture. For the movement with the sitting posture, as the estimation of intersegmental L5/S1 joint moments is not possible while the participant is in contact with the seat, the beginning of the stand phase was identified as the moment when the participant was no longer in contact with the seat. The end of the return phase was identified as the moment immediately before the participant was in contact with the seat.

For MMH tasks, the instant when the dummy was fully supported by the participant during the lift phase, and the moment when the dummy was no longer fully supported by the participant during the deposit phase, were also manually identified from the videos.

Phases where the participant did not begin with a static position (most of the time for the first repetition) were not considered.

## 2.5. Markerless assessment

From videos, kinematic data were estimated using Theia3D (Theia Markerless v2023.1) which sequentially performed person identification, 2D keypoints position estimation, 3D keypoints reconstruction, and an inverse kinematics step using a multibody kinematics optimization and a custom scaled biomechanical model. The data exported from Theia3D consisted of the pose matrices of each segment of the biomechanical model. From these pose matrices, joint angles were computed with Biorbd (Michaud and Begon, 2021). Inertial parameters were added according to (Lahkar et al., 2022a) who proposed to adapt the anthropometric table of Dumas and Wojtusch (2018) to match as best as possible to the biomechanical model available in Theia3D.

Due to inconsistencies observed during pre-processed data, joint velocities and joint accelerations were chosen to be fixed to zero. In other words, inverse static rather than inverse dynamics was computed, neglecting the inertial but no the gravitational terms of the dynamic equations.

Intersegmental joint moments were computed using a recursive Newton-Euler algorithm with a top-down approach. For all movements involving a dummy (MMH and STS tasks), the forces it applied to the hands were modeled by artificially adding half of the dummy's mass to each hand (keeping the center of mass location the same). For MMH tasks, the artificial masses were added only when the dummy was fully supported by the participant. The joint moments analyzed were those between the pelvis and the abdomen, expressed at the L5/S1 joint center. Further details and a step-by-step description of the markerless assessment can be found in Supplementary Material 1.

Kinematic data estimated by Theia3D may be incomplete (i.e., no data available for one or more instants for a part or all segments of the biomechanical model). If any phase contained incomplete data, that phase was considered invalid. Considering valid phases, only the first repetition was analyzed.

## 2.6. Marker-based assessment

Joint angles were estimated from the 3D marker positions using a multibody kinematics optimization approach (Begon et al., 2018), implemented with Biorbd (Michaud and Begon, 2021). The definition of the biomechanical model (segment reference frames and inertial parameters) was defined according to Dumas and Wojtusch (2018).

Joint coordinates were low-pass filtered (4th-order Butterworth with no phase shift and a cut-off frequency of 6 Hz) and then numerically derived to calculate joint velocities and joint accelerations.

Intersegmental joint moments were computed using a recursive Newton-Euler algorithm with a bottom-up approach. Measured ground reaction forces (preliminarily filtered using a 4th-order Butterworth filter with no phase shift and a cut-off frequency of 12 Hz) were used as the external forces acting on each foot. The joint moments analyzed were those between the pelvis and the abdomen, expressed at the L5/S1
<caption>**Table 1**</caption>

RMSD and Pearson correlation coefficient (r) of L5/S1 joint moment estimates between markerless and marker-based approaches for MMH tasks.

<table><caption>**Table 2**</caption><thead><tr><th>Posture<br>Load (kg)</th><th>All</th><th>Standing<br>2.7</th><th>10</th><th>Kneeling<br>2.7</th><th>10</th><th>Squatting<br>2.7</th><th>10</th></tr></thead><tbody><tr><th scope="row">RMSD (Nm)</th><td>22.4</td><td>25.3</td><td>24.9</td><td>16.2</td><td>21.0</td><td>19.4</td><td>27.4</td></tr><tr><th scope="row">r</th><td>0.95</td><td>0.96</td><td>0.96</td><td>0.94</td><td>0.94</td><td>0.97</td><td>0.91</td></tr></tbody></table>

RMSD and Pearson correlation coefficient (r) of L5/S1 joint moment estimates between markerless and marker-based approaches for STS tasks.

<table><thead><tr><th>Posture<br>Load (kg)</th><th>All</th><th>Sitting<br>0</th><th>2.7</th><th>10</th><th>Kneeling<br>0</th><th>2.7</th><th>10</th><th>Squatting<br>0</th><th>2.7</th><th>10</th></tr></thead><tbody><tr><th scope="row">RMSD (Nm)</th><td>19.7</td><td>22.8</td><td>22.1</td><td>33.0</td><td>12.2</td><td>14.1</td><td>21.8</td><td>14.1</td><td>13.2</td><td>23.2</td></tr><tr><th scope="row">r</th><td>0.69</td><td>0.58</td><td>0.57</td><td>0.66</td><td>0.59</td><td>0.61</td><td>0.64</td><td>0.83</td><td>0.91</td><td>0.88</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Bland-Altman plot comparing the peak L5/S1 joint moments estimated by the two approaches.</figcaption></figure>

joint center.

The same repetition as that analyzed by the markerless assessment was considered. Marker-based estimates were downsampled to match the markerless frequency.

## 2.7. Data analysis

For both markerless and marker-based assessments, the norm along the three axes of the intersegmental L5/S1 joint moment was calculated, and named L5/S1 joint moment in the remainder of the paper. For each segmented phase, L5/S1 joint moments assessed with both approaches were compared considering Root Mean Square Difference (RMSD) and a Pearson correlation coefficient (r).

The peak value of the estimated L5/S1 moment during the phase was then identified, a metric commonly used as an indicator of risk factors for the development of MSDs (Kim et al., 2022). Peak L5/S1 joint moments assessed with both approaches were compared using a Bland Altman analysis. Bland-Altman bias, confidence interval (1.96 times standard deviation or 1.45 times interquartile range for non-normal distributions), coefficient of determination, and RMSD were calculated.

## 3. Results

Considering all combinations of movement – load, mean RMSD is 21.1 ± 12.1 Nm, mean Pearson coefficient correlation was 0.80 ± 0.23 for L5/S1 joint moment. For MMH tasks (Table 1), the average RMSD was 22.4 Nm across all tasks, with values ranging from 16.2 Nm to 27.4 Nm depending on the movement – load combination. Regardless of the movement or load, correlations were higher than 0.9, with a mean of

0.95.

For STS tasks (Table 2), the average RMSD was 19.7 Nm across all tasks, ranging from 12.2 Nm to 22.8 Nm depending on the movement – load combination, except for sitting – 10 kg, where the mean RMSD was 33 Nm. Correlations ranged from 0.57 to 0.66 for sitting and kneeling postures, and from 0.83 to 0.91 for the squatting posture.

For the assessment of peak L5/S1 joint moments, the Bland-Altman analysis (Fig. 4) showed a bias of 7.36 Nm, a confidence interval of 50.9 Nm, a coefficient of determination of 0.56, and RMSD of 26.9 Nm. Figures in Supplementary Material 2 (S1-S6) illustrate, for each movement – load combination, the assessed peak L5/S1 joint moments obtained with both approaches.

## 4. Discussion

The aim of this study was to develop and evaluate a markerless method for estimating low back intersegmental moments in childcare-related activities. This approach enhances the ecological validity of the analysis by reducing experimental constraints. Based on laboratory experiments, the L5/S1 joint moments obtained with the proposed method (video-based markerless motion capture) were compared to those obtained with the reference approach (marker-based motion capture combined with force platforms). The main findings showed that mean RMSD values of L5/S1 joint moments were about 20 Nm, and RMSD for peak values were 27 Nm. These results suggest that the proposed video-based markerless approach provides a reasonably accurate estimation of low back intersegmental moments and could be a valuable solution for studying childcare-related tasks with high ecological validity.

In the literature, several methods have been proposed to estimate L5/S1 joint moments using systems suitable for field deployment. These approaches were based either on IMUs (Faber et al., 2016; Muller et al., 2022; Nail-Ulloa et al., 2024) or, as in the present study, on videos (Mehrizi et al., 2017). The errors reported by Faber et al. (2016) were lower than those observed in the present study (RMSD below 10 Nm and RMSD for peak values around 10 Nm). However, their tasks involved only trunk bending without external loads. The results reported by Muller et al. (2022) (RMSD of 21 Nm and RMSD of 26.5 Nm for peak values), by Mehrizi et al. (2017) (RMSD of 28 Nm for peak values), and by Nail-Ulloa et al. (2024) (mean difference of 19 Nm and mean difference of 26 Nm for peak values) were very similar to those obtained with the current method. All three approaches were evaluated on MMH tasks, and to the best of our knowledge, this type of method has not yet been evaluated on STS tasks. The errors observed in the present study for STS tasks are of the same order of magnitude as those reported for MMH tasks. Although lower correlations were found for sitting and kneeling postures in STS tasks, the RMSD values were of similar magnitude to those obtained for other movements and postures. Lower correlations observed for sitting and kneeling postures compared to squatting can be explained by the smaller moment amplitudes in these conditions.
Indeed, for the same level of error, larger amplitudes lead to higher correlations.

The differences observed between the proposed method and the reference method arise from several sources. First, a top-down approach was used rather than a bottom-up approach, commonly considered the gold standard for this type of analysis. Even when using a reference measurement system (marker-based motion capture combined with force sensors), average differences between 5 Nm and 24 Nm can be observed between bottom-up and top-down approaches (Kingma et al., 1996; Larivière and Gagnon, 1998; Plamondon et al., 1996). These differences come from the fact that the most influential parameters in estimating L5/S1 intersegmental moments differ depending on the approach: localization of the L5/S1 joint center relative to the center of pressure in the bottom-up approach, versus localization of the upper body segment centers of mass and the external load relative to the L5/S1 joint center in the top-down approach. Depending on the measurement and/or estimation method, errors in these parameters can be significant. However, the use of a top-down approach is due to the experimental constraints associated with measuring ground reaction forces with force platforms. Second, the use of a top-down approach requires estimating the position of the load, here the dummy. For reasons of methodological simplicity and usability, its position was estimated from the position of the hands. This assumption is commonly made in the literature for in situ applications (Muller et al., 2022; Skals et al., 2021). However, in these studies, participants held a load directly in their hands, with a standardized grip and, in some cases, handles. In the case of grasping a dummy (see illustration of the grasp in Fig. 1 and Fig. 2), the assumption of considering the load's position as the midpoint between the two hands can introduce larger errors. This explains why larger load masses lead to higher errors (point also reported in the literature (Larsen et al., 2020; Muller et al., 2022)). Nonetheless, the assumption done allows for a systematic process of all trials. Third, the proposed markerless method is quasi-static. As a consequence, it underestimates intersegmental moments by neglecting acceleration-related terms. This underestimation is particularly observable in the STS tasks (Figs. S4-S6 in Supplementary Material 2). However, this approach is necessary due to the numerous kinematic inconsistencies observed in the output of Theia3D software. These inconsistencies are likely related to the presence of the dummy, which causes both occlusion issues in the detection of upper limb key-points and potential mixing of keypoints between the participant and the dummy. Such an application, involving a participant holding a baby or a dummy, is not a standard case in computer vision and would require dedicated methodological developments to improve pose estimation accuracy.

The main advantage of the proposed approach is that it does not require any instrumentation or prior preparation of the participants or the babies. This advantage ensures good ecological validity of the study. Although IMUs are commonly used in the literature for in situ analyses, they require participant instrumentation phases, calibration procedures, as well as a potential discomfort for the participant during task execution. In a work context (especially one involving contact with babies) these limitations may be restrictive. In contrast, the use of markerless technology only requires equipping the room with cameras and performing their calibration. This setup can be done once prior to a working day and has very limited impact on professionals and children. While the uncertainties associated with this type of method are not negligible, it enables movement quantification in less controlled settings. Note that these uncertainties must be considered in the analysis and interpretation of the results. Thus, if the differences observed between two situations are of the same order of magnitude as the errors found here, they should be interpreted with caution, as they may merely reflect measurement error rather than actual changes in behavior. It can be observed (Figs. S1-S6 in Supplementary Material 2) that the effects of the posture or dummy mass on the L5/S1 joint moments remained the same between markerless and marker-based methods. For instance, peak L5/S1 joint moments were maximised when handling a 10 kg dummy from a

standing posture and minimized when moving from kneeling to standing without a charge. Therefore, although underestimated, the L5/S1 joint moments computed with the markerless method can be used to compare different conditions.

Using this method in practice requires knowing some essential information such as the participant's body mass, the baby's mass, as well as the start and end instants of tasks. The masses of the participant and the baby can be collected with questionnaires. Instants were identified manually in this study, which can be a time-consuming process. Although solutions have been proposed, such as the development of automatic task detection algorithms (Muller et al., 2019), their implementation in early childhood care settings remains challenging, as ways of holding babies are highly diverse and there are many phases when childcare workers only partially support the baby.

This study has some limitations. In particular, the use of the proposed method in a real work setting would require further evaluation in a less controlled environment. The controlled environment was necessary to compare the proposed methods with a reference method (skin markers, force platforms), as the latter would have been more difficult to deploy in real-world occupational than in laboratory settings. First, only the participant under analysis and one baby dummy were present within the camera field of view. In a real-world context, multiple professionals may be present along with several babies. Thus, with a markerless approach, occlusions will be more frequent, and the multi-person aspect will need to be addressed. Second, a dummy was used in this study. While the dummy allows reproduction of handling similar to that with a baby, it does not replicate the baby's potential movements. Due to the assumptions made regarding the dynamics of the load, these potential movements of the baby will not be considered and could increase the errors made. Third, the tasks performed were limited compared to those typically observed in daycare settings (for instance, only symmetrical tasks were considered). Fourth, the participants were not early childhood professionals, and the movements performed may therefore have differed from those observed in real work settings. To minimize this impact, the task design was based on observations in real working conditions, and participants were given instructions aligned with these observations. A complementary evaluation under less standardized conditions could be performed in this regard. In addition, the method could be adapted to account for situations commonly encountered in daycare settings, such as handling multiple babies or holding a baby with multiple points of contact (arms, thighs, torso) (Pardo Ramos, 2025).

## 5. Conclusion

In the present study, a markerless method for estimating low back intersegmental moments in childcare-related activities was developed and evaluated in controlled conditions. Compared to a reference approach, differences were around 20 Nm for the assessment of L5/S1 intersegmental moments. Considering this uncertainty, the method enables the quantification of low back intersegmental moments without any instrumentation of the participants. Future work will focus on evaluating the feasibility of applying this method under in situ measurement conditions, involving significant occlusions and less controlled activities.

### CRediT authorship contribution statement

**Diana Pardo Ramos:** Writing – review & editing, Writing – original draft, Software, Methodology, Formal analysis, Conceptualization. **Léan Mazier-Vallet:** Writing – review & editing, Software, Methodology, Formal analysis. **Adriana Savescu:** Writing – review & editing, Methodology, Formal analysis, Conceptualization. **Raphaël Dumas:** Writing – review & editing, Methodology, Formal analysis, Conceptualization. **Antoine Muller:** Writing – review & editing, Writing – original draft, Software, Methodology, Formal analysis, Conceptualization.
# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jbiomech.2025.113070.

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