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Gait models and reference motions are essential for the objective assessment of walking patterns and therapy progress, as well as research in the field of wearable robotics and rehabilitation devices in general. A human can achieve a desired gait speed by adjusting stride length and/or stride frequency. It is hypothesized that sex, age, and physique of a person have a significant influence on the combination of these parameters. A mathematical description of the relation between gait speed and its determinants is presented in the form of a parameterized analytic function. Based on the statistical significance of the parameters, three models are derived. The first two models are valid for slow to fast walking, which is defined as the interval of approximately 0.6–2.0ms−1, assuming a linear relation of gait speed and stride length, and a non-linear relation of gait speed and stride duration, respectively. The third model is valid for a defined range of walking speed centered at a certain (preferred or spontaneous) gait speed. The latter assumes a constant walk ratio, i.e. the ratio between step or stride length and step or stride frequency, and is recommended for walking at a speed of 1.0–1.6ms−1. On the basis of a large pool of gait datasets, regression coefficients with significance for age and/or body mass index are identified. The presented models allow to estimate the gait cycle duration based on gait speed, sex, age and body mass index of healthy persons walking on even ground.

This study aimed to evaluate whether combining force–velocity Fv and force-position Fp models, originally developed for single-joint movements, could effectively characterize force production during the push-off phase of a vertical jump. Six force–velocity-position Fv,p models, integrating three Fv (Anderson, Hill, and Linear) and two Fp (Cosine and Quadratic) models were assessed. Fifteen trained CrossFit athletes performed maximal countermovement jumps under varying loads and push-off depths with ground reaction forces recorded via force plates. All six models demonstrated high goodness-of-fit, with r2 ranging from 0.885 to 0.886 and RMSE values ranging from 262.6 to 266.5 N, effectively capturing key experimental data characteristics. No significant differences in fitting or descriptive capacity were observed among Anderson, Hill, and Linear models, reflecting the near-linear behavior of the force–velocity relationships in vertical jump. Nevertheless, the Linear model offers simplicity and interpretability by focusing on key physiological parameters (e.g., maximal force, maximal velocity, and optimal position) commonly used in applied sports contexts. The Cosine and Quadratic models showed no significant impact on overall fit quality, although significant differences in optimal vertical position (popt) and theoretical maximal force (Fmax) were observed. When paired with the Linear model, the Quadratic model slightly reduced Fmax deviations in participants with slightly curvilinear force–velocity relationships. This study highlights the strength of a simple three-parameter heuristic model, whose parameters are biomechanically and physiologically relevant, in describing the force production as a function of position and velocity. This combination of simplicity and interpretability represents a significant step forward in the modeling of multi-joint movements, offering practical insights for sport performance optimization.

In musculoskeletal simulations, IMU-based approaches are often compromised by errors such as joint angle drift and offset errors due to calibration inaccuracies. These errors can compromise the accuracy of both kinematic and dynamic outcomes. This study presents a simulation-based investigation that uses synthetic inertial and positional data to systematically assess the potential of integrating spatial reference information into IMU-driven inverse kinematics analyses. Optical motion capture data was captured and error-free kinematic and dynamic data was created based on the optical motion capture data. The error-free data was then used as a reference. Based on this reference data, synthetic orientation and position data was created, incorporating a range of error types and magnitudes (e.g., sensor noise, drift, misalignment). To create the IMU-based analysis results, we calculated relative quaternions based on the orientation data which were then converted into Euler angles. We then conducted a sensitivity analysis to determine the spatial accuracy required in the position data to effectively compensate for typical IMU errors. Across all error types and magnitudes, the multimodal inverse approach (using both synthetic IMU and positional data) yielded significantly more accurate results than solely IMU-based analyses. Specifically, the mean joint angle RMSE decreased by , the mean joint torque RMSE by , the mean residual force RMSE by , and the mean residual torque RMSE by . Future research will evaluate the effectiveness of the multimodal inverse kinematics approach when applied to real-world measurement data.

Subject-specific parameters influence spinal loads and the risk of back disorders but their relative effects are not well understood. The objective of this study is to investigate the effects of changes in age (35–60 years), sex (male, female), body height (BH: 150–190cm) and body weight (BW: 50–120kg) on spinal loads in a full-factorial simulation using a personalized (spine kinematics, geometry, musculature and passive properties) kinematics driven musculoskeletal trunk finite element model. Segmental weight distribution (magnitude and location along the trunk) was estimated by a novel technique to accurately represent obesity. Five symmetric sagittal loading conditions were considered, and main effect plots and analyses of variance were employed to identify influential parameters. In all 5 tasks simulated, BW (98.9% in compression and 96.1% in shear) had the greatest effect on spinal loads at the L4–L5 and L5–S1 levels followed by sex (0.7% in compression and 2.1% in shear), BH (0.4% in compression and 1.5% in shear) and finally age (<5.4%). At identical BH and BW, spinal loads in females were slightly greater than those in males by ~4.7% in compression and ~8.7% in shear. In tasks with no loads in hands, BW-normalized spinal loads further increased with BW highlighting the exponential increase in spinal loads with BW that indicates the greater risk of back disorders especially in obese individuals. Uneven distribution of weight in obese subjects, with more BW placed at the lower trunk, further (though slightly <7.5%) increased spinal loads.

There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate the shoulder load in wheelchair-related activities of daily living using wearable sensors. Ten able-bodied participants equipped with five inertial measurement units (IMU) on their thorax, right arm, and wheelchair performed activities exemplary of daily life of MWUs. Electromyography (EMG) was recorded from the long head of the biceps and medial part of the deltoid. A neural network was trained to predict the shoulder load based on IMU and EMG data. Different cross-validation strategies, sensor setups, and model architectures were examined. The predicted shoulder load was compared to the shoulder load determined with musculoskeletal modeling. A subject-specific biLSTM model trained on a sparse sensor setup yielded the most promising results (mean correlation coefficient = 0.74 ± 0.14, relative root-mean-squared error = 8.93% ± 2.49%). The shoulder-load profiles had a mean similarity of 0.84 ± 0.10 over all activities. This study demonstrates the feasibility of using wearable sensors and neural networks to estimate the shoulder load in wheelchair-related activities of daily living.

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

Subcutaneous injection of monoclonal antibodies (mAbs) has attracted much attention in the pharmaceutical industry. During the injection, the drug is delivered into the tissue producing strong fluid flow and tissue deformation. While data indicate that the drug is initially uptaken by the lymphatic system due to the large size of mAbs, many of the critical absorption processes that occur at the injection site remain poorly understood. Here, we propose the MPET 2 approach, a multi-network poroelastic and transport model to predict the absorption of mAbs during and after subcutaneous injection. Our model is based on physical principles of tissue biomechanics and fluid dynamics. The subcutaneous tissue is modeled as a mixture of three compartments, i.e., interstitial tissue, blood vessels, and lymphatic vessels, with each compartment modeled as a porous medium. The proposed biomechanical model describes tissue deformation, fluid flow in each compartment, the fluid exchanges between compartments, the absorption of mAbs in blood vessels and lymphatic vessels, as well as the transport of mAbs in each compartment. We used our model to perform a high-fidelity simulation of an injection of mAbs in subcutaneous tissue and evaluated the long-term drug absorption. Our model results show good agreement with experimental data in depot clearance tests.

Epidemiological studies are divided over the causative role of body weight (BW) in low back pain. Biomechanical modeling is a valuable approach to examine the effect of changes in BW on spinal loads and risk of back pain. Changes in BW have not been properly simulated by previous models as associated alterations in model inputs on the musculature and moment arm of gravity loads have been neglected. A detailed, multi-joint, scalable model of the thoracolumbar spine is used to study the effect of BW (varying at five levels, i.e., 51, 68, 85, 102, and 119kg) on the L5–S1 spinal loads during various static symmetric activities while scaling moment arms and physiological cross-sectional areas of muscles using in vivo imaging data. The L5–S1 loads substantially increased with BW especially in flexed postures. As BW increased from 51 to 119kg, the L5-S1 compression increased in flexed postures by ~80–147% with no load in hands and by ~46–52% in load holding tasks. In obese individuals with body mass index>30kg/m2 spinal loads further increased by up to 15% as lever arms for gravity loading at the waistline (T12 through L5) increased by 2cm (for BW=102kg) and 4cm (for BW=119kg). With changes in BW, spinal loads would have moderately altered (<17%) had identical muscle parameters been considered. Since scaling muscle parameters demands additional efforts in modeling, one could opt for simulation of alterations only in BW while using some averaged musculature values.

In cataract surgery, post-surgical stability of the intraocular lens plays a major role. This study aims to explore how the size and decentration of the capsulorhexis affect intraocular lens decentration and tilt by using numerical methods. Finite element models included zonules, ciliary body, capsular bag, and an IOL with two open-loop haptics were built. Capsulorhexes were modeled with a 4.5- and 5.5-mm diameter. The capsulorhexis was shifted 0.5–1 mm in two in-plane directions normal to the optical axis. Three IOLs with different powers (5 D, 29 D, and 34 D) were compared. The results were also compared with currently published numerical and clinical studies. With different capsulorhexes sizes and locations, the decentration varied from 0.43 to 8.3 μm, and the tilt varied 0.02° − 0.09°. The 34 D lens had the largest tilt and decentration when capsulorhexis changed sizes or decentered. The simulation showed that capsulorhexis size and decentration have only a minor effect on IOL decentration or tilt that will in most cases not be noticeable to the patient.

Motion of the mandible and temporomandibular joint (TMJ) plays a pivotal role in the function of the dentition and associated hard and soft tissue structures, and facilitates mastication, oral communication and access to respiratory and digestive systems. Quantification of TMJ kinematics is clinically relevant in cases of prosthetic rehabilitations, TMJ disorders, osteoarthritis, trauma, tumour resection and congenital abnormalities, which are known to directly influence mandibular motion and loading. The objective of this systematic review was to critically investigate published literature on historic and contemporary measurement modalities used to quantify in vivo mandibular and TMJ kinematics in six degrees of freedom. The electronic databases of Scopus, Web of Science, Medline, Embase and Central were searched and 109 relevant articles identified. Publication quality was documented using a modified Downs and Black checklist. Axiography and ultrasonic tracking are commonly employed in the clinical setting due to their simplicity and capacity to rapidly acquire low-fidelity mandibular motion data. Magnetic and optoelectronic tracking have been used in combination with dental splints to produce higher accuracy measurements while minimising skin motion artefact, but at the expense of setup time and cost. Four-dimensional computed tomography provides direct 3D measurement of mandibular and TMJ motion while circumventing skin motion artefact entirely, but employs ionising radiation, is restricted to low sampling frequencies, and requires time-consuming image processing. Recent advances in magnetic tracking using miniature sensors adhered to the teeth in combination with intraoral scanning may facilitate rapid and high precision mandibular kinematics measurement in the clinical setting. The findings of this review will guide selection and application of mandibular and TMJ kinematic measurement for both clinical and research applications.