Developmental joint control in two-foot vertical jumps of 3-5-year-old children.

Towards more effective skill discovery in reinforcement learning by incorporating state reachability.

Hoof trimming and shoeing techniques are used to manage and prevent equine limb injuries. However, quantitative studies comparing the effects of different shoeing techniques on equine joint biomechanics over the full gait cycle are lacking. To measure and compare joint motion and net torques at the distal forelimb joints when horses walk overground unshod, with a standard flat shoe, and with a rocker shoe. In vivo study. Gait data were recorded from 12 sound horses during walking. Three shoeing conditions were tested: unshod, flat shoe, and rocker shoe. Data were recorded for each shoeing condition immediately after trimming (short hoof condition) and again after 6 weeks of hoof growth (long hoof condition). Three-dimensional motion capture and retro-reflective skin markers recorded left forelimb motion, while synchronised force plates measured the corresponding ground reaction force. Inverse dynamics was used to calculate the net torques developed about the distal forelimb joints. Statistical comparisons were performed with multilevel mixed effects generalised linear models. While there were limited effects of trimming and shoeing, the rocker shoe was associated with higher walking speed (by 9.3 ± 9.7%) and reduced stride duration (by 4.9 ± 6.9%) compared with the flat shoe for the short hoof condition (p < 0.001). Throughout the stride cycle, the fetlock joint was less extended (by 9.0 ± 13.7°) while the distal interphalangeal joint (DIPJ) was more extended (by 10.7 ± 16.6°) for both shoeing types compared to unshod regardless of hoof growth (p < 0.005). Higher peak torques were generated at the DIPJ for flat shoe compared to unshod (by 0.05 ± 0.27 Nm/kg) in the short hoof condition, and for flat shoe compared to rocker shoe (by 0.03 ± 0.14 Nm/kg) in the long hoof condition (p < 0.05 for both). The horses were tested at a low-speed walking gait. Forelimb joint biomechanics did not differ substantially across the three shoeing and two hoof-growth conditions. Future studies should test the robustness of these findings at the trot and canter.

Studying drift maneuvers helps improve vehicle handling and broaden the dynamic performance envelope of autonomous vehicles. This paper presents a hierarchical drift control scheme for distributed drive electric vehicles, based on terminal sliding mode control (TSMC). A saturation velocity planner is developed to transform pose errors into bounded reference states, thereby improving stability in drifting trajectory tracking. Subsequently, a TSMC is designed to track these reference states, leveraging its finite-time convergence and robustness. To further improve robustness and adaptability, an radial basis function neural network (RBFNN) approximator is incorporated into the framework for real-time compensation of modeling inaccuracies and unmodeled dynamics. In addition, an optimization-based inverse vehicle dynamics model is employed to map the desired state derivatives to vehicle control inputs. Lastly, co-simulation experiments in Simulink and CarSim demonstrate that the proposed method outperforms baseline control schemes in trajectory tracking and drifting stability.

Human squat motion is commonly analyzed using inverse dynamics, where joint moments are computed from experimentally measured kinematics. Such analyses typically assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. In this study, a computational framework is proposed for the load-constrained reconstruction of squat motion that integrates kinematic motion generation with a mechanical model of moment-limited joints. The human body is represented as a multi-segment system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational springs with bounded moment capacity, allowing elastic response followed by allowing bounded moment response and redistribution of mechanical demand as critical moment levels are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization problem is solved at each motion frame to obtain a mechanically admissible posture under external loading. The objective function combines trajectory tracking with joint energy contributions, while gravitational loading from a barbell applied at the shoulders introduces external work. The formulation enables automatic correction of the reference motion when joint moment limits are exceeded, resulting in mechanically admissible squat postures. Numerical examples illustrate the evolution of pelvis trajectory, torso inclination, lower-limb segment angles, and reconstructed body configurations throughout the squat cycle. The results confirm that joint moment capacity directly influences the reconstructed motion and leads to load-dependent adaptation of squat posture.

Spring-damper titin model improves the estimation of muscle forces during force enhancement measurements.

This study presents a neural network-based inverse dynamics method for identifying unbalance faults in flexible slider-crank mechanisms. Slider-crank mechanisms are central in reciprocating machinery, and their dynamics can be altered by unbalance and link flexibility. We develop a Lagrangian model of a flexible slider-crank with an extensible spring-damper connecting link and a parametric unbalance mass on a triangular plate, then train a feedforward neural network to recover five coupled physical parameters (unbalance mass, angular position, eccentricity, period time, and damping ratio) simultaneously from a single 51-point displacement trace. On clean synthetic data, the network achieves R &gt;0.97 for all five parameters with sub-millisecond inference. A spatial error analysis maps prediction accuracy to unbalance position on the link, revealing that errors concentrate near the crank and slider joints where kinematic sensitivity is lowest. A noise robustness study shows that the clean-data network fails entirely under measurement noise; retraining with noise-augmented data (30–60 dB SNR) restores useful accuracy for four parameters at realistic noise levels, while unbalance mass remains vulnerable due to its weak coupling to slider displacement. These results establish quantitative bounds on the method’s applicability and identify supplementary sensing requirements for experimental implementation.

While spiking neural networks (SNNs) have demonstrated remarkable efficiency in neuromorphic computing by emulating biological neuronal dynamics, their learning capabilities remain constrained by predominant focus on synaptic plasticity. This limitation overlooks critical neurobiological evidence showing that intrinsic neuronal plasticity, particularly dynamic threshold adaptation, plays an essential role in balancing neural responsiveness and signal fidelity. Inspired by two neurophysiological principles governing threshold regulation: 1) the inverse correlation between spiking thresholds and preceding depolarization rates, and 2) the proportional relationship between thresholds and average membrane potentials, we propose a Membrane Potential-Driven Adaptive Threshold Plasticity (MPD-ATP) framework. This biologically grounded mechanism establishes a dual-pathway control system where instantaneous depolarization rates and sustained membrane potential states jointly modulate neuronal thresholds through an adaptive scaling factor. The instantaneous depolarization rate dynamically lowers thresholds during strong input bursts, while the sustained average membrane potential adjusts the baseline threshold to stabilize firing during sparse input. This complementary regulation improves precision and robustness. Extensive evaluations on static (CIFAR-10/100) and neuromorphic (CIFAR10-DVS, DVSGesture) benchmarks demonstrate that MPD-ATP-enhanced networks achieve superior classification accuracy with enhanced noise robustness. Systematic ablation studies reveal that the coordinated interaction between depolarization-sensitive and membrane potential-proportional threshold adjustments is critical for preventing signal saturation in high-activity networks while mitigating under-activation in sparse-input scenarios.

To achieve optimal stability and mobility after total shoulder replacement, a comprehensive understanding of post-operative glenohumeral biomechanics is required, which cannot be easily obtained directly from in vivo measurements. Therefore, we developed a musculoskeletal multibody simulation framework of the upper extremity after anatomical total shoulder replacement that combines a six-degree-of-freedom glenohumeral joint, detailed muscle representations, a frictional contact model between implant components, and a computed muscle control algorithm. The framework integrates inverse and forward dynamics to track predefined kinematics, while solving the muscle distribution problem through static optimization. Using this framework, outcome measures such as muscle forces, joint contact forces, contact pressure and area, center of pressure trajectory, and humeral head translations were estimated for one subject performing a 120° thoracohumeral arm elevation in the scapular plane. The reliability of the presented simulation framework was assessed by tracking prescribed kinematics and by indirect validation of the findings against previously reported data from experimental and in vivo instrumented implant measurements, and similar simulation studies. The computed muscle control algorithm accurately reproduced the desired joint trajectories, with a tracking error of less than 0.2°, and demonstrated high computational performance. Estimated glenohumeral joint quantities generally fell within the ranges reported in literature and reproduced features such as posterior-superior loading on the glenoid and small translations consistent with concavity-compression stabilization. The results suggest that the presented framework can provide realistic estimates of glenohumeral biomechanics after total shoulder replacement and may serve as a basis for future work on patient-specific analysis of implant design, positioning, and soft-tissue conditions.

Investigating the effects of smartphone-related dual tasks on dynamic stability and biomechanical indices during backpack-carrying walking.

Abstract Background Biomechanical alignment is routinely assessed through visual observation, yet this positional paradigm fails to account for dynamic force-regulation mechanisms governing musculoskeletal behaviour under progressive load. Objective To present the MMSX Alignment Spectrum — a five-grade mechanistic framework classifying alignment based on force-vector trajectories, torque distribution, and tissue tolerance proximity. Methods Narrative synthesis of peer-reviewed biomechanical, orthopaedic, sports science, and motor control literature including EMG, inverse dynamics, and tissue tolerance modelling evidence. Results Alignment is load-dependent. The Grade C-to-D transition represents a critical inflection from performance inefficiency to active injury mechanism activation, undetectable via positional observation alone. Conclusions Alignment is a continuous, load-dependent force-regulation outcome. The MMSX Spectrum provides a scalable, evidence-congruent instrument for injury prevention and performance optimisation.

This study investigates the common assumption that segmental inertial parameters remain constant during manual lifting using a model-based experimental approach. The primary objective was to evaluate the variability in these parameters and the subsequent effects on biomechanical calculations. The research was conducted with 20 participants (10 females and 10 males) who performed lifting tasks in the two-dimensional sagittal plane under three distinct load conditions: 2.5 kg, 5.0 kg, and 7.5 kg. Angular variations of the hand, arm, and leg joints were recorded using video-based image processing techniques. These kinematic data, integrated with anthropometric measurements, were incorporated into Newton–Euler-based equations of motion to determine joint reaction forces and net joint moments. During the initial forward dynamics stage, the solvability of the problem was tested using constant mass ratios from the established literature. In the following inverse dynamics stage, genetic algorithms were utilized to overcome solution diversity and identify the variable inertial parameters responsible for the observed motion. The results indicate that changes in segment moments of inertia reached 18–37%, leading to variations of 0–19% in net joint moments. These findings highlight the critical necessity of incorporating dynamic inertial parameters into accurate biomechanical moment calculations for manual materials handling.

We report the observation and assignment of high resolution rotation-vibration-tunnelling spectra of aniline ( C 6 H 5 NH 2 ) with the Zürich GHz spectrometer from 75 GHz to 500 GHz and by high resolution Fourier Transform Infrared (FTIR) THz spectroscopy from 0.8 to 42 THz (26 to 1400 cm − 1 , in part synchrotron based at our highest resolution spectrometer at the Swiss Light Source, SLS, achieving a best possible resolution Δ ν ~ = 0.00052 cm − 1 and effective resolutions generally better than 0.0008 cm − 1 ). Rotation-vibration tunnelling transitions have been assigned and analysed for the vibrational ground state, the associated inversion tunnelling level I 1 at 40.953 cm − 1 as well as the lowest frequency out of plane CN-bending fundamental 10 b 1 at 216.466 cm − 1 and the torsional fundamental at 275.930 cm − 1 and the corresponding inversion tunnelling sublevels. High resolution results are also obtained for the excited levels I 2 ( 423.046 cm − 1 ) and I 3 ( 699.369 cm − 1 ) and further vibrational modes interacting with the tunnelling motion. Accurate band centres and rotational parameters of the effective Hamiltonian are presented for numerous vibrational-tunnelling levels. The results are discussed in terms of the tunnelling quantum dynamics of inversion at the nitrogen atom with a pyramidal equilibrium structure for this prototypical aromatic amine. We discuss mode selective promotion or inhibition of tunnelling by excitation of different vibrational modes. We also discuss the accurate spectroscopic results as possible benchmarks for theoretical treatments of tunnelling in complex polyatomic molecules and the possibilities of analysing rotation vibration-tunnelling spectra in dense complex spectra of transiently chiral molecules and molecules potentially useful as candidates for the measurement of molecular parity violation.

The hip, knee, and ankle joint moments during gait are critical for clinical decision-making in patients with cerebral palsy (CP). These moments are typically calculated using inverse dynamics and human body models based on ground reaction forces (GRF). However, obtaining GRF data from CP patients can be challenging. Recent studies suggest joint moments in CP patients can be predicted using joint angles alone, bypassing the need for GRF, through machine learning (ML). However, the optimal type and scope of input data for such models remain unclear. This study aimed to identify the most feasible ML approach based on prediction accuracy for predicting joint moments from gait kinematics in CP patients. Retrospective gait data from 917 CP patients were analysed; after applying inclusion-exclusion criteria, data from 622 CP patients were used. We evaluated four conventional ML algorithms, ridge regression, k-nearest neighbors, random forest, and multilayer neural network, using feature-based input, and two deep learning algorithms, one-dimensional convolutional neural network and long short-term memory network, using raw data input, with each model’s hyper-parameters optimized in a problem-specific manner. Models were assessed using normalized root mean square error (nRMSE) and Pearson correlation coefficient (PCC). Deep learning models achieved an average nRMSE of 14.75 ± 7.10 and PCC of 0.95, while conventional ML models yielded 16.03 ± 6.55 and 0.94, indicating both conventional and deep learning approaches showed promise for predicting joint moments in patients with CP. By considering factors such as data availability and computational cost, an appropriate ML method can be selected to effectively address gait kinetics prediction in individuals with CP.

This study investigated how joint dynamics and kinematics change with increasing squat intensity in elite powerlifters performing the powerlifting-style low-bar back squat. Twenty-nine national- and international-level Austrian athletes (13 women, 16 men) executed single-repetition squats at 70–90% of their estimated one-repetition maximum (1-RM) according to International Powerlifting Federation standards. A three-dimensional motion capture system and two force plates recorded full-body kinematics and ground-reaction forces. Inverse kinematics and inverse dynamics analyses were used to quantify joint angles and hip, knee and ankle flexion/extension moments. Absolute and relative joint flexion/extension moments were compared across intensities using repeated-measures ANOVA and Statistical Parametric Mapping (p < 0.05). As the load increased, hip joint flexion/extension moments increased significantly (p < 0.001) during the concentric phase, while knee and ankle flexion/extension moments remained unchanged. Relative joint flexion/extension moments shifted from the knee and ankle towards the hip, indicating a more hip-dominant strategy. No significant gender × intensity interaction was observed. These results demonstrate that during near-maximal squats in elite powerlifters, the hip joint endures the highest flexion/extension moments among the primary lower limb joints. Our findings can help inform coaching strategies for high-level athletes.

Human locomotion relies on a proximal–distal organization of joint mechanical work that adapts to task constraints, such as those imposed by inclined walking. In individuals with transtibial amputation, loss of the biological ankle disrupts this organization, leading to proximal alterations and inter-limb asymmetries. Active mechatronic prosthetic feet have been developed within a biomechanical biomimicry framework to restore distal positive mechanical work. This exploratory study quantified the effects of an active mechatronic prosthetic foot on joint mechanical work during inclined walking. Four individuals with transtibial amputation performed instrumented treadmill walking at −3°, 0°, and +3° using their habitual passive foot and a powered foot. Positive and negative mechanical work at the ankle, knee, and hip were computed using inverse dynamics and compared with a normative reference database (n = 20). The powered foot induced modest, task-dependent modifications, mainly at the ankle and knee. In downhill walking, it promoted a more symmetrical redistribution of negative mechanical work, particularly at the knee, suggesting a partial reduction in contralateral overload. In uphill walking, distal assistance increased prosthetic-side positive work, reflecting slope-dependent reallocation rather than normalization. Although a multivariate deviation score indicated reduced deviation under the powered condition, full convergence toward the asymptomatic organization was not achieved.

Sensorimotor control models traditionally consist of two types of internal models: inverse models, which compute the motor commands needed to reach a desired movement goal, and forward models, which predict the resulting sensory feedback. These models are usually considered separate entities, but it is unclear whether such separation exists in the nervous system. Additionally, maintaining separate networks may be more computationally expensive. Therefore, we investigated whether these functions could be executed within a single neural circuit: an inverse-forward-recognition model (InFoRM). We implemented InFoRM using neural networks and compared their ability to reproduce cyclic reaching movements with that of control architectures based on classical, separated inverse and forward models. Desired movement trajectories were represented by recorded three-dimensional kinematics, while efferent (muscle activation) and afferent (muscle length and velocity) signals were obtained through inverse dynamics. Our findings show that InFoRM significantly outperforms control architectures across various conditions, while requiring fewer resources. The network is also able to morph to untrained movement directions, generating motor commands and predicted feedback that had not been learned. These findings demonstrate the computational advantages of integrating inverse and forward processes within a single neural network, suggesting that such unified sensorimotor models may be worthwhile to explore further.

This study aimed to investigate the effects of exercise-induced fatigue on the risk of tibial stress fractures by comparing biomechanical parameters during backhand-side smash landings in elite female badminton players before and after fatigue. Thirteen elite female badminton players performed smash-landing trials before and after fatigue. The AnyBody Modeling System was used for inverse dynamics analysis, and finite element analysis was conducted using the AnyFE2Abq plugin to compute tibial stress and strain. After fatigue, the backhand rearcourt jump smash (BRJS) and the backhand lateral jump smash (BLJS) landing movements showed significantly increased peak tibial acceleration (p = 0.003; p = 0.008), vertical average loading rate (p = 0.024; p = 0.040), and vertical instantaneous loading rate (p = 0.007; p = 0.029. Moreover, the simulated tibialis anterior muscle force significantly decreased after fatigue (p = 0.004; p = 0.046). Finite element analysis further revealed markedly increased tibial stress and strain after fatigue, with peak stress and strain reaching multiple times the pre-fatigue values (peak stress: BRJS anterior medial, 224.0%; BRJS posterior, 213.2%; BLJS anterior medial, 348.6%; BLJS posterior, 522.8%; peak strain: BRJS, 241.1%; BLJS, 243.7%). Exercise-induced fatigue increased tibial acceleration and vertical loading rates during smash landings in elite female badminton players. Tibial stress and strain also increased, suggesting a potentially higher risk of tibial stress fracture.

Background/Objectives: Quasi-static inverse dynamics is widely used in biomechanical analyses due to its computational simplicity; however, neglecting inertial effects may introduce joint-specific torque estimation errors during dynamic movements. The purpose of this study was to quantify torque estimation errors introduced by quasi-static assumptions during bodyweight squats performed at different movement frequencies. Methods: A planar MATLAB-based (version R2022a) musculoskeletal model incorporating standard anthropometric parameters was developed to simulate squat motions at 1.00, 0.75, 0.50, and 0.25 Hz. Joint torques calculated using quasi-static inverse dynamics were compared with fully dynamic inverse dynamics at the ankle, knee, and hip. Model agreement was evaluated using Root Mean Square Error (RMSE), normalized percentage error relative to peak dynamic torque, and bootstrapped 95% confidence intervals (CI). Results: Quasi-static modeling produced negligible torque estimation errors at the ankle and knee across all movement frequencies, with percentage errors consistently below 0.1% and narrow confidence intervals. In contrast, the hip joint demonstrated a clear frequency-dependent underestimation of torque when inertial effects were neglected. At 1.00 Hz, the hip RMSE reached 14.4 Nm, corresponding to 14.01% of peak dynamic torque (95% CI: 13.97–14.06%). Error magnitude increased systematically with movement speed. Conclusions: The validity of quasi-static inverse dynamics strongly depends on joint location and movement frequency. While quasi-static models are appropriate for ankle and knee torque estimation during moderate-speed squats, accurate hip torque assessment during faster squats requires full dynamic modeling. These findings provide quantitative benchmarks to inform model selection in biomechanical research, rehabilitation engineering, and assistive device design.

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC &gt; 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC &gt; 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation.