Accurate estimation of medial and lateral knee compartment loading is crucial for understanding joint mechanics. The traditional two-step approach, which first estimates musculotendon forces and then derives joint reaction forces and moments, is often followed by a simplified 2D load-balancing method to compute tibiofemoral contact forces in the frontal plane. This study investigated the effect of full 3D knee equilibrium on tibiofemoral contact and ligament force estimation. Gait data from 48 healthy participants were analyzed using a lower-limb musculoskeletal model. The tibiofemoral joint was modeled as a hinge with a moving axis. Joint reaction forces and moments were obtained by subtracting musculotendon forces estimated via static optimization from the intersegmental loads derived through inverse dynamics. Medial and lateral contact forces were then computed either with the 2D or the proposed 3D load-balancing method, which uses all joint reaction load components to estimate ligament and contact forces. The 3D method yielded significantly higher medial and lateral contact forces throughout stance, peaking at 2.50 BW and 1.82 BW, respectively. Ligament forces peaked at 1.05 BW for the ACL, 1.12 BW for the PCL, and 0.44 BW for the LCL, with the MCL largely unloaded. Non-negligible least-squares residuals were observed, indicating limitations in force balance. The 3D load-balancing method revealed physiologically implausible loading patterns arising from joint reaction loads, particularly elevated internal/external rotation moments, computed after independently estimating musculotendon forces in the classical two-step approach. The 2D load-balancing approach is blind to these outcomes. Future work should favor one-step approaches that simultaneously resolve musculotendon, ligament, and contact forces.

Seat pan shear force is a major contributor to seating discomfort, but the relationship between the seat pan and seat back angles that eliminates seat pan shear force across a wide range of reclined postures remains unclear. This study investigated this relationship using a musculoskeletal (MSK) seated human model to simulate seat configurations with seat back inclinations ranging from 0 to 60 in a step of 10 degrees. The MSK model predicted a parabolic relationship between seat back angle and the zero-shear seat pan angle, consistent with experimental observations (R2 = 0.77 and RMSE = 1.99 degrees). The model also predicted the expected redistribution of body weight from seat pan to seat back when increasing seat back inclination, although the discrepancies exist between prediction and measurement in absolute contact forces. These findings suggest that the MSK model is capable of predicting zero-shear seat pan angle across a wide range of seat back inclinations, while highlighting the need to improve contact definitions and optimization strategy to better represent comfort-oriented seating configurations.

This work aimed to establish the relationship between seat back and seat pan angles to eliminate the seat pan shear force for a large range of seat configurations. Thirty-nine volunteers participated in the experiment. A reconfigurable experimental seat was used. Seven seat back angles ranging from 0 (vertical) to 60° were tested. For each, starting from a preferred seat pan angle self-selected by participants, an experimenter manually adjusted it so that the shear on the seat pan surface fell within [-5N, 5N] while the pelvis was controlled to keep in contact with the seat back. Results suggested a parabolic relationship between seat back and seat pan angles with a peak of about 14.9° at 40 degrees of seat back angle. On average, the zero-shear seat pan angle was 4.2° higher than the preferred one and the use of a leg support increased zero-shear angle by 4.4°.

BACKGROUND:Video-based markerless motion capture is rapidly emerging as a valuable tool in biomechanics research, particularly within sports science, ergonomics, and clinical evaluations. Markerless motion capture offers greater versatility for capturing movement outside of laboratory settings and with minimal setup. This technique involves a series of steps to derive 3D joint angles from 2D video images: 2D keypoint identification, triangulation, and inverse kinematics. RESEARCH QUESTION:What methodologies are used at each step of the process from 2D images into 3D joint angles? METHODS:This narrative review critically analyses the methodologies employed at each step of the process based on 12 methodological articles of the literature. These articles are representative of typical commercial or research approaches currently available. RESULTS:Most markerless approaches use OpenPose to identify the 2D keypoints, some of them correct their inconsistencies either in 2D or 3D or increase their number with augmented 3D landmarks. Few approaches use a confidence-weighted triangulation. Almost all of them perform multibody kinematics optimisation, classically tracking the 3D keypoints/landmarks, or maximising the confidence derived from all the images. SIGNIFICANCE:This process shares certain similarities with traditional marker-based motion capture. The methodologies are adapted to a limited number of keypoints with unclear anatomical definition and potential physically implausible trajectories. The methodologies are also adapted to incorporate the confidence information. Most adaptations rely on neural networks complementary to the human pose estimators. Markerless motion capture methodologies are close to what is commonly applied to marker-based data acquired with opto-electronic cameras but mostly rely on data-driven statistical inference. The evaluation is currently limited to comparison with marker-based kinematics, mainly on young adults without pathology.

Musculoskeletal disorders affect childcare workers at the lower back and the lower limbs. Childcare professionals lift children while adopting standing, kneeling and squatting postures. The risks associated with lifting from standing postures have been extensively evaluated, but evidence remains insufficient regarding the adoption of kneeling or squatting postures. This study aimed to estimate peak intersegmental moments at the L5/S1, hips and knee joints during a lifting activity in these three postures. This biomechanical evaluation was conducted under controlled laboratory conditions using two baby dummies of 2.7 kg and 10 kg. Joint kinetics was assessed using tri-dimensional video data and ground reaction forces. Results suggest that both lifting posture and dummy's load impact peak intersegmental magnitudes in all the joints. Lifting from a standing posture using the 10 kg baby dummy presented the highest peak intersegmental moments at the L5/S1 and the hip joints, with associated magnitudes of 228 Nm and 131 Nm respectively. These magnitudes decreases around 20% in squatting and 40% in kneeling postures. In contrast, the solicitations at the knee joint were higher in squatting (75 Nm) and kneeling (92 Nm) postures. These orders of magnitude were comparable to those found in the literature using classical loads of equal or greater mass, likely due to the postural exigence related to the type of load (a baby dummy) and grip (under the armpits). However, considering the work specificities in a nursery, the current recommendations for manual material handling cannot be directly followed for childcare workers.

Muscle force sharing is typically resolved by minimizing a specific objective function to approximate neural control strategies. An inverse optimal control approach was applied to identify the “best” objective function, among a positive linear combination of basis objective functions, associated with the gait of two post-stroke males, one high-functioning and one low-functioning. By comparing objective-function-predicted muscle forces to those of a reference EMG-driven model, using Root-Mean-Squared-Errors (RMSE) and Pearson Correlation Coefficients (CC), it was found that the “best” objective function is subject- and leg-specific. No single function works universally well, yet the best options are usually differently weighted combinations of muscle activation- and power-minimization. Subject-specific inverse optimal control models performed best on their respective limbs (RMSE 178/213 N, CC 0.71/0.61 for respective legs of subject 1; RMSE 205/165 N, CC 0.88/0.85 for respective legs of subject 2), but cross-subject generalization was poor, particularly for paretic legs. Moreover, minimizing the root mean square of muscle power emerged as important for paretic limbs, while minimizing activation-based functions dominated for non-paretic limbs. This may suggest different neural control strategies between affected and unaffected sides, possibly altered by the presence of spasticity.Among the 15 considered objective functions commonly used in inverse dynamics-based computations, the root mean square of muscle power was the only one explicitly incorporating muscle velocity, leading to a possible model for spasticity in the paretic limbs. Although this objective function has been rarely used, it may be relevant for modeling pathological gait, such as post-stroke gait.

Multi-camera markerless motion capture commonly triangulates 3D points from 2D keypoint positions in multiple camera views, then applies a multibody kinematics optimization (MKO) to incorporate biomechanical constraints. However, standard pipelines neglect the 2D confidence heatmaps generated by human pose estimation networks. We hypothesized that performing MKO in 2D camera planes would make it more robust to missing keypoints and allow us to obtain better accuracy. 2D confidence heatmaps were used to maximize available information. To test this, we first model each network-derived heatmap as a 2D Gaussian function characterized by its center, amplitude, and standard deviation. Second, we maximize the sum of these modeled confidences after projecting the biomechanical model into the camera planes. To demonstrate feasibility, we evaluated our method on data from two participants performing sit-to-stand, walking, and manual material handling, captured by a two-camera setup, and simultaneously collected marker-based data. Our Gaussian modeling of the heatmaps demonstrated a mean absolute difference of 0.011 compared to the original discrete maps, confirming its validity. In terms of 3D joint positions and angles, the confidence-based MKO produced results similar to classical distance-based methods. Notably, the confidence-based approach overcame occultations: 89.3% of frames could only be obtained with the distance-based MKO due to missing keypoints, while the confidence-based MKO computed 100% of frames. These findings underscore the potential of using full 2D confidence heatmaps in markerless motion capture, especially under challenging conditions such as sparse camera setups.

Lumbar spine loading is considered as an indicator of sitting discomfort. The internal loading can be measured using force sensor implants, but this is an invasive method thus limited to a very small number of studies. Alternatively, spinal loading can be estimated non-invasively using a musculoskeletal (MSK) model. However, few studies evaluated a MSK model by comparing estimated and measured spine loading in seated conditions. This study aimed to evaluate a MSK model by comparing the trend of the simulated resultant force at L1 vertebra with in-vivo measurements in ten different seating conditions. Results show a highly correlated (r = 0.98) trend between the model prediction and experimental ground truth, suggesting the MSK model has possibility to be used for seating research.

The soft tissue artefact is a well-known issue for marker-based motion analysis and markerless motion analysis is by definition free from this artefact. The goal of this study is to compare the limb skeletal inconsistencies generated by the neural networks in markerless motion capture and generated by the soft tissue artefact in marker-based motion capture using retrospective data. Sixteen volunteers were included and were asked to perform four motor tasks (walk, sit-to-stand, stand-to-sit, countermovement jump) acquired with ten optoelectronic cameras and ten video cameras. Keypoint identification was performed in videos using Openpose. Triangulation and data augmentation algorithms were used to get an extension of anatomical landmarks. Then, lower limb skeletal inconsistencies (length variations and apparent joint dislocations) for both marker-based and markerless data were analyzed. The length variation of the lower limbs was generally larger with markerless data (triangulated keypoints and augmented anatomical landmarks) as found with marker-based data. Mean dislocations were found smaller for the markerless data than for the marker-based data for the hip only. The effect of the markerless inconsistencies are at least as large as the effect of the soft tissue artefact except for the hip dislocation, probably due to the soft tissue artefact that is main at the pelvis level. These inconsistencies are related to different phenomena than skin sliding as there are no correlation with joint flexion-extension angles. Thus, compensation methods proposed for soft tissue artefact are not all applicable.

Background: To our knowledge, no study is available comparing the change in ankle mechanics during gait after total ankle arthroplasty (TAA) based on the origin of the osteoarthritis. As the nature of trauma is different in patients sustaining post-fracture ankle osteoarthritis (PFOA) from those sustaining post-sprain ankle osteoarthritis (PSOA), it could be expected that the outcomes of TAA, in terms of ankle mechanics during gait, would be different in the 2 groups. A prospective matched comparative study was therefore performed to investigate whether patients sustaining PFOA had different outcomes in terms of changes to ankle mechanics during gait (before surgery vs 1 year after surgery), compared with patients sustaining PSOA.Methods: Fifteen patients with PFOA and 15 patients with PSOA scheduled for primary TAA for pain relief were recruited and peer-matched based on their demographic and spatiotemporal data. All patients underwent a 3D gait analysis before and after surgery, during which a kinematic and kinetic multi-segment foot model was used to quantify inter-segmental joint kinematics and kinetics.Results: The PFOA group exhibited significantly lesser pre- vs postoperative increases in ankle (Shank-Calcaneus) joint peak power, and ankle (Shank-Calcaneus) joint work after TAA compared with the PSOA group. Furthermore, the results demonstrated a trend toward greater increases in peak ankle (Shank-Calcaneus) joint plantarflexion moment and in negative ankle (Shank-Calcaneus) joint work for the PSOA group compared with the PFOA group.Conclusion: This study suggests that patients sustaining PFOA have smaller pre- to postoperative gains in ankle (Shank-Calcaneus) joint power and ankle (Shank-Calcaneus) joint work during gait after TAA compared with patients sustaining PSOA, with modest between-group effects. Although evidence in TAA is lacking, insights from knee replacement suggest prehabilitation and nutritional support may mitigate deficits, representing a potentially essential strategy for PFOA patients requiring further validation.

This paper investigates statistical methods for analyzing the spatial coherence function to estimate myocardial fiber orientation, a critical factor in understanding cardiac microstructure and advancing cardiac imaging diagnostics. Four approaches, namely Correlation, Mutual Information, Kraskov Mutual Information, and Granger Causality, were evaluated using an in vitro experimental dataset designed to simulate myocardial fiber alignment. Our findings reveal that Granger Causality effectively captures complex and highly anisotropic structures, such as corners, while mutual information-based methods demonstrate superior stability and consistency across simpler regions. By applying a filter based on fractional anisotropy, the performance of each method was refined, highlighting their distinct strengths in fiber tracking. To leverage these complementary advantages, we propose the Fused and Consensus methods, which integrate the strengths of individual approaches to enhance coherence analysis and improve fiber orientation estimation. This study shows that the four methods complement each other by excelling in different aspects of fiber tracking, offering a robust framework for accurately characterizing fiber orientation. These insights can potentially improve the assessment of myocardial microstructure and aid in the early diagnosis and treatment of cardiac diseases.

Body segment inertial parameters (BSIPs) are critical for human movement analysis. However, child-specific BSIPs remains limited. This study aimed to develop regression models for BSIPs (mass, CoM-position, and moments of inertia) using 3D body scans from 688 children aged 2.9-12.7 years. A 3D scanning system was used to capture body surfaces as point clouds, which were automatically processed to generate segmented, personalized volumetric body meshes with embedded segment coordinate systems. These meshes were then used to compute 3D BSIPs, which were normalized (relative to body mass and corresponding segment length) and fitted by regression models separately for males and females. The regression models demonstrated high predictive accuracy for normalized mass and moderate-to-good accuracy for normalized CoM-positions and radii of gyration. Age-related changes were observed as reductions in normalized mass for the head-neck and abdomen, alongside increases for the thigh. Normalized CoM-positions shifted posteriorly for the abdomen, anteriorly for the thigh, and proximally for the forearm. Normalized radii of gyration declined across all directions, particularly for the hand and thigh. This work provides the first comprehensive BSIP regressions for a large, gender-balanced cohort of children up to 12 years old, addressing limitations in prior research with a fully automated approach. These regressions are expected to advance biomechanical modeling and enhance movement analysis in pediatric populations.

This study aims to explore the potential for accurately estimating joint angles during upper limb rehabilitation tasks with different calibration procedures, inverse kinematics methods and measurement modalities. Affordable embedded visual-inertial measurement units offer a promising alternative to the costly and cumbersome gold standard marker-based optical motion capture systems. However, affordability comes with inherent sensors inaccuracies. Hence, prior to their application in a real clinical setting, it is important to demonstrate their ability for accurate joint angle estimation. Discrepancies in joint angles arise due to the inaccuracies of different sensing modalities but also to sensor-to-segment calibration procedures that significantly alter the joint offsets. Therefore, in this paper, the impact of functional and anatomical calibration procedures on joint angle estimation was compared among seven healthy young volunteers. When the same calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, a relatively small root mean square error of 7.9 deg and correlation coefficients exceeding 0.86 were observed. When different calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, higher root mean square superior to 10 deg were observed, highlighting the importance of consistency with the reference set when assessing accuracy. Furthermore, our analysis shows the benefit of using multi-body inverse kinematics procedure over treating inverse kinematics separately for each segment when dealing with inaccurate visual-inertial measurement units data.

We present a dataset designed for benchmarking markerless motion capture methods (from videos to joint kinematics). The dataset includes both raw and processed data. Two participants performed five tasks - walking, sit-to-stand, manual material handling, handstand hold or Y-pose (depending on the participant), and a jointly performed dance sequence. Movements were captured simultaneously recorded using 10 optoelectronic cameras (Qualisys Miqus M3, 120 Hz) and 9 video cameras (Qualisys Miqus Video, 60 Hz, 1920×1088 pixels). The raw dataset provides 3D marker trajectories and video recordings. The processed dataset includes joint kinematics obtained from both marker-based motion capture and 7 different markerless methods, contributed by multiple research teams as part of a challenge organized during a national biomechanics seminar. Additionally, the open-access GitHub repository containing processed data enables researchers to contribute new markerless methods estimated and expand the dataset collaboratively. This resource aims to facilitate benchmarking and support the development of robust markerless motion analysis methods.