Journal of Biomechanical Engineering

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

In the current clinical practice, the diagnosis of spinal disorders and their surgical planning are critically based on imaging data. To complement this data, patient-specific finite element models have been developed and showed to be powerful tools for evaluating spine mechanics. Most of them rely on Computational Tomography (CT) scans - which have a high resolution but are seldom available in routine clinical practice - while only a recent few models are on less invasive Magnetic Resonance Imaging (MRI). Yet, despite the proliferation of these computational models, encompassing detailed anatomical and functional information, the rheological assumptions they are built upon are based on tissue-sample mechanical response data, which leaves a gap in the quantitative analysis on how such assumptions influence the macroscopic response of a functional spinal unit. Aiming at addressing these shortcomings, the main purpose of this work is to introduce a quantitative computational assessment of the macroscopic impact of commonly adopted rheological models - from linear elasticity to fiber-reinforced nonlinear hyperelasticity - in several loading conditions, focusing on a lumbar unit which is considered as a typical benchmark system. We also propose a reconstruction procedure to accurately describe subject-specific anatomy from MRI data, including the intervertebral disc and its nucleus pulposus. Bones are modeled as linear elastic media, whereas for the AF, we consider three different mechanical models - namely, isotropic linear elasticity and the Holzapfel-Gasser-Ogden model with and without fiber reinforcement. Model verification on an idealized geometry demonstrates numerical consistency, while parametric orthostatic simulations highlight the need for nonlinear formulations to capture anisotropy and strain-stiffening behavior of the intervertebral disc. Then, we carry out flexion, lateral bending, and torsion tests on a subject-specific reconstructed functional unit, for which we provide parametric analysis in terms of momentum magnitude and resulting range of motion. These tests further confirm the need for a nonlinear rheology of the annulus fibrosus and provide a quantitative assessment of the differences between the constitutive laws considered. Moreover, successful comparisons with the literature, in terms of macroscopic deformation under several loading conditions, serve as partial validation for our computational model.

Experimental studies involving mechanical loading of the human head and brain in vivo are necessarily limited, making computational modeling essential for advancing our understanding of brain biomechanics. Demographic factors such as age and gender are known to influence brain anatomical structures, material properties, and potentially vulnerability to injurious loading. To address this, we construct six group-average brain models stratified by age and gender from a total of 135 subjects, and investigate the mechanical responses of these "group-average brains" using computational simulations. We use Pearson correlations to assess to what degree the group-average models represent individuals within each demographic category, showing strong correlations. Further, our p-value hypothesis test of the first principal strain across the six groups shows significant differences. This study demonstrates that age- and gender-stratified group-average models can effectively represent biomechanical responses of the individuals within the groups, and can reveal meaningful demographic differences that may influence susceptibility to traumatic brain injury. We show that the age-dependent change in material properties plays a greater role than anatomical changes in driving differences in the deformations. We hope to see increased utilization of these group-average models in both research and clinical applications.

Background/PurposeDiagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). MethodsPersonalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. ResultsAverage area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. ConclusionThis study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Patellar osteochondral allograft (OCA) transplantation is widely used to treat large full-thickness cartilage defects, yet long-term failure and reoperation rates remain high. Although surface congruity and osseous integration are emphasized clinically, cartilage thickness and mechanical compatibility between donor and recipient are not considered. Our previous work suggests that cartilage thickness mismatch can amplify local deformation at the graft boundary, potentially compromising graft longevity. This study investigates how combined mismatches in cartilage thickness and mechanical properties influence the local strain environment at the patellar OCA interface. Simplified two-dimensional axisymmetric finite element models of patellar OCA repair were developed in ABAQUS. Donor-to-recipient cartilage thickness ratios ranging from 0.33 to 3.25 were evaluated together with donor-recipient Youngs modulus mismatches (2.5-7.0 MPa). Cartilage was modeled using homogeneous linear elastic and functionally graded material formulations to account for depth-dependent stiffness. A compressive pressure of 1.0 MPa was applied to represent patellofemoral joint loading, and peak compressive and shear strains were quantified at the graft boundary. Cartilage thickness mismatch produced localized high-strain regions (HSR) of compressive and shear strain at the donor-recipient interface that were absent in thickness-matched constructs. Strain amplification increased with both thickness and mechanical property mismatch. Compressive strain exhibited directional asymmetry, with donor-side-thicker configurations producing greater amplification than recipient-side-thicker configurations. Incorporating depth-dependent cartilage stiffness reduced peak strain magnitudes but did not eliminate mismatch-driven strain amplification. These findings demonstrate that cartilage thickness and mechanical disparity can create HSR at the patellar OCA graft boundary that may predispose grafts to impaired integration and long-term failure.

The human masseter muscle is one of the primary muscles responsible for mastication and mandibular movement; however, its intrinsic mechanical properties remain insufficiently characterized. In this experimental study, the nonlinear, viscoelastic, and history-dependent behaviour of the human masseter muscle was investigated using ex vivo uniaxial cyclic tensile tests. The masseter muscle samples prepared from fresh and formalin-preserved cadavers were tested under two loading protocols: a continuous stretch protocol with increasing stretch levels and a constant stretch protocol with repeated loading to a fixed maximum stretch. Tests were conducted at two strain rates, and their influences on the mechanical behaviour of the tissue were examined. The effect of formalin preservation was also investigated. The results showed that the stiffness of the tissue increases for formalin-preserved samples. Under cyclic loading, the features including energy dissipation, stress-softening, residual deformation, and cyclic conditioning progressively changed during the initial loading cycles and reached stabilization during the final cycle. These findings provide experimental evidence that the human masseter muscle exhibits nonlinear, viscoelastic, and history-dependent mechanical behaviour under cyclic tensile loading. The experimental data obtained in this study may be used for biomechanical modelling of the human masticatory system and the development of constitutive models for cranio-maxillofacial surgical simulation, prosthetic design, and facial soft-tissue biomechanics. Statement of significanceThe masseter muscle is one of the primary muscles of mastication. To address the current gap in craniofacial biomechanics that has largely focused on the mechanical characterization of the masseter muscle based on imaging techniques or monotonic loading, this study quantifies the nonlinear and viscoelastic mechanical response of masseter tissue under cyclic continuous and constant stretch loading, including strain-rate and preservation effects. The results show that the mechanical behaviour of the masseter muscle, including stiffness, hysteresis, stress-softening, and residual strain behaviour, is strongly influenced by strain-rate and formalin preservation. The experimental results provide mechanical data for constitutive modelling of the masticatory system with applications in cranio-maxillofacial surgical simulation, prosthetic design, and facial soft tissue modelling.

PurposeFunctional tricuspid regurgitation (FTR) is driven by right ventricular (RV) remodeling, annular dilation, and papillary muscle dislocation. Free wall approximation (FWA) has been proposed to treat FTR by addressing RV dilation, but its effects on tricuspid valve (TV) biomechanics remain unclear. We present a real-time 3D echocardiographic (rt3DE)-based finite element framework to quantify TV biomechanics under FTR, and preliminarily apply it to assess FWA effects. MethodsSubject-specific models were developed from rt3DE data of three dilated porcine hearts in an ex-vivo mock-loop. TV geometries at end-diastole and peak systole (PS) were complemented by parametric chordae tendineae and hyperelastic tissue properties. TV closure was simulated under a standard pressure load and image-based annular motion. After tuning chordae length to replicate the PS ground truth in FTR, FWA was simulated as 30% and 60% approximations along three anatomical directions (anterior-posterior, A-P; anterior-septal, A-S; anterior-septal wall, A-SW). ResultsIn FTR simulations, median geometric errors ranged from 1.16 to 1.26 mm; median stress ranged from 56.4 to 74.7 kPa. FWA simulations predicted regurgitant orifice area (ROA) reductions by 53-99%, albeit overestimating the residual ROA vs. in vitro ground truth when starting from particularly extreme FTR conditions; concomitantly, a median stress reduction by 8-43% vs. FTR conditions was predicted. ConclusionPreliminary data suggest that our rt3DE-based framework can reliably quantify FTR-related TV biomechanics and that post-FWA biomechanics depends on initial FTR conditions. A larger cohort is required to verify the method and obtain statistically significant results.

Muscle force sharing during locomotion is influenced by the mechanical demands of movement and the contractile properties of synergistic muscles. In cats, plantarflexor muscles exhibit distinct functional specialization, with the slow-fibred soleus maintaining relatively constant force across conditions while faster muscles such as the plantaris and gastrocnemius increase force production with increasing locomotor demand. However, it remains unclear whether similar force-sharing patterns occur in larger animals with different musculoskeletal designs. Therefore, the purpose of this study was to examine force sharing between the superficial digital flexor (SDF) and medial gastrocnemius (MG) muscles during treadmill locomotion in sheep. Tendon buckle force transducers were surgically implanted on the SDF and MG tendons of seven sheep, and in vivo muscle forces were recorded during walking and trotting across different speeds and inclines. Both muscles increased force with increasing speed and incline; however, speed had a substantially greater effect than incline. The SDF consistently produced greater absolute force than the MG across all conditions, whereas the MG exhibited slightly larger relative increases in force with increasing speed. Time to peak force decreased with increasing speed in both muscles, although the SDF reached peak force later in stance than the MG across conditions. In contrast to the distinct specialization observed in cats, neither muscle displayed a relatively condition-independent, soleus-like force contribution. These findings suggest that force sharing in sheep is more distributed across synergistic muscles and may reflect the influence of musculoskeletal design, tendon compliance, and mixed fibre-type composition on muscle function in larger species.

I.Bone fragility is a major ang growing public health concern for many with degenerative bone diseases. A better understanding of fracture propagation in bone can better inform treatment options for these individuals. Human bone is an anisotropic material, with directional microstructure that fractures can propagate around. In a healthy individual, the bone remodeling process repairs damaged tissue. Calcium sulfate dihydrate is a well established surrogate material for bone. This study will mechanically investigate damage repaired plaster samples using three point bending to characterize crack propagation around repaired damage.

Total knee replacement restores mobility in patients with advanced osteoarthritis, yet many individuals still experience limited ability to perform high-flexion tasks such as squatting. Current preoperative planning relies on static imaging and cannot predict how different implant alignment choices will affect postoperative dynamic function. This study developed a predictive simulation framework that uses bi-level inverse optimal control to link preoperative implant alignment directly to expected postoperative squat kinematics. Subject-specific musculoskeletal models were constructed for six total knee replacement patients using experimental squat data. Bi-level inverse optimal control was applied to identify both individualised and group-level cost functions. The individualised setting provided subject-specific accuracy, while the group-level setting derived a single group-level cost function as an initial step toward preoperative use without requiring postoperative motion data. The individualised setting reproduced experimental trajectories with low errors across all joints (mean apex difference 1.53{degrees}, root-mean-square error 5.15{degrees}, normalised root-mean-square error 11.15%, Pearson correlation 0.96). The group-level setting yielded higher but acceptable errors (mean apex difference 5.70{degrees}, root-mean-square error 6.75{degrees}, normalised root-mean-square error 17.53%, Pearson correlation 0.95) while preserving the general pattern and phasing of the motion. Squat depth emerged naturally from the optimisation rather than being prescribed. This framework may provide a basis for future quantitative tools to evaluate how implant alignment choices influence postoperative squat performance, potentially improving functional outcomes in total knee replacement. These results suggest that the proposed IOC framework can reproduce key features of post-TKR squat kinematics, but further out-of-sample validation is required before it can be used for preoperative prediction or translated into tools aimed at improving functional outcomes in total knee replacement.

Pediatric patients with epicardial cardiac implantable electronic devices (CIEDs) are frequently excluded from the Magnetic Resonance Imaging (MRI) primarily due to RF heating safety concerns. In this study we evaluate RF heating of two bipolar epicardial leads during MRI at 0.55 T and 1.5 T under different termination conditions. Our findings showed that the mean RF heating was significantly reduced at 0.55 T MRI compared to that at 1.5 T. Similarly, the RF heating at 0.55 T MRI was highest for full system whereas, during MRI at 1.5 T, the RF heating was highest for the capped abandoned lead, showing dependence of RF heating pattern on MRI field strength. While RF heating at both fields surpassed the safety limit, the capped abandoned leads at 1.5 T MRI showed significantly higher RF heating with temperature rise surpassing 50{degrees}C in some of the cases. These results highlight the difference in RF heating of bipolar epicardial leads compared to the previously reported findings for monopolar epicardial lead which showed smallest heating for capped abandoned lead at both field strengths. These findings emphasize the necessity of device-specific evaluations at each field-strength to inform clinical decision-making and expand MRI access for this vulnerable population.

Aortic stiffness is both heterogenous and anisotropic. Current non-invasive methods to estimate aortic stiffness are limited to characterizing the aortic tissue as isotropic due to the lack the techniques required to extract multi-axial strain from 3D dynamic images. Vascular deformation mapping (VDM) is a nonrigid image registration technique which has thus far been applied to map aortic growth using longitudinal imaging. In this study, we propose to use VDM to assess 3D aortic deformation by mapping diastolic and systolic images. During image registration process, penalty parameters are employed to fine-tune image alignment and penalize non-physiological deformations. These penalty parameters must be calibrated to ensure that VDM successfully reproduces multi-axial aortic motion patterns in health and disease. In this paper, we developed a calibration pipeline for these parameters using synthetic data. A rotation-free shell model was used to generate physics-based synthetic data on aortic motion incorporating patient-specific geometries, root motion, and blood pressure from a cohort of 14 subjects (healthy, Marfans syndrome and thoracic aortic aneurysm). An error metric was defined to quantify the quality of the VDM results. Furthermore, a k-means clustering technique was used to categorize the subjects into three clusters based on ascending aortic motion. Optimal penalty parameters were identified for each of the three clusters. The results indicated that patient clusters with smaller aortic root motion required larger rigidity penalty values. The calibrated parameters successively reduced errors in 3D displacement and multi-axial stretch compared to un-optimized VDM predictions, enhancing the accuracy of capturing aortic deformation from dynamic images. Among the different aortic regions, the ascending thoracic aorta exhibits the largest error reduction.

Knee joint injury is a major risk factor for post-traumatic osteoarthritis (PTOA), often associated with early cartilage degeneration. Mechanical overloading and cytokine-driven inflammation are key drivers of this process, yet the underlying mechanisms and their distinct temporal and spatial contributions to cartilage degradation remain unclear. Here, we present a mechanobiological finite element framework that simulates cartilage degradation through cell-mediated proteolytic activity triggered by mechanoinflammation and cytokine-driven inflammation. The model reproduces experimentally observed depth-dependent loss of collagen and aggrecan, with mechanoinflammation inducing a transient response and cytokine-driven inflammation sustaining prolonged matrix degradation. Sensitivity analysis further shows that mechanoinflammation-driven degradation is governed mainly by protease production per cell, whereas cytokine-driven degradation is more sensitive to the rate of cellular stimulation. Together, this framework provides a mechanistic basis to study proteolytic cartilage degeneration and supports future in silico evaluation of therapeutic strategies aimed at mitigating cartilage degradation in PTOA.

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

Rotator cuff tendinopathy is highly prevalent in aging populations, yet the mechanisms leading to age-dependent tendon degeneration are not well understood. In addition to tensile loading, tendons are subjected to compressive forces at certain anatomical sites (e.g., Achilles, rotator cuff), where altered adaptive responses may contribute to degenerative remodeling. The objective of this study was to investigate age-related differences in tendon responses to dynamic compressive loading using an ex vivo model. Murine flexor tendon explants from young and aged animals were cultured in a biaxial bioreactor and subjected to different levels of dynamic compressive loading. We then observed changes in metabolic activity, matrix composition, matrix biosynthesis, matrix structure, and gene expression. Young tendons exposed to moderate levels of compression maintained homeostasis, whereas high compression induced a robust adaptive response characterized by increased glycosaminoglycan accumulation, elevated collagen content, and upregulation of remodeling-associated genes including collagen I, decorin, and MMP-9, as well as inflammatory and apoptotic markers. In contrast, aged tendons demonstrated a qualitatively different response, with transcriptional downregulation of key remodeling markers alongside elevated secretion of matrix-degrading enzymes and pro-inflammatory cytokines, indicative of a maladaptive mechanobiological response even at low compressive levels. These findings reveal that impaired mechanosensitivity and a lower threshold for injury may predispose chronically loaded tissues to degenerative pathology associated with excessive compressive loading.

The aorta, normally resilient to hemodynamic stresses, becomes vulnerable to structural failure due to diverse conditions that weaken the wall. We injected fluid into excised specimens of human ascending aorta with pressure monitoring to quantify the impact of clinical and histological factors on mural damage. Two modes of medial injury emerged with distinct pressure tracings. Extravasation was characterized by diffuse infiltration of fluid with widespread damage of smooth muscle cells and collagen fibers but limited separation of elastic lamellae. By contrast, delamination was characterized by marked separation of elastic lamellae along a single plane with damage to cells and fibrillar matrix restricted to adjacent laminae. Aging, aortic dilatation, and family history associated with lower pressures causing delamination, whereas a diagnosis of hypertension associated with higher pressures suggesting resilience to dissection. Collagen fraction adjacent to delamination correlated with higher pressures as did decreased smooth muscle cell density and increased glycosaminoglycan fraction, although several clinical and histological variables were interrelated. Protein cross-linking strengthened and enzymatic digestion of collagen weakened the aortic wall, while acute cell lysis with detergent had no effect. We conclude that increased functional medial collagen has an adaptive protective role in aortic remodeling rather than signifying medial degeneration.

Various ankle-foot conditions (e.g., fractures, diabetic foot ulcers, and post-surgical recovery) require periods of complete non-weightbearing followed by gradually increasing partial loadings. However, existing assistive devices often provide inconsistent or uncomfortable offloading during gait. Additionally, prolonged proximal leg offloading can contribute to muscle atrophy, reduced bone density, and overuse of other body segments. We present a novel offloading ankle-foot orthosis (OLAFO) designed to overcome these limitations. The OLAFO features a patient-specific load-bearing shank brace, designed through a digital workflow and fabricated from a 3D-printed core reinforced with carbon-fiber composite lamination. Interlocking serrated side struts, adjustable in 2 mm increments, modulate load sharing between the shank and plantar surfaces. Furthermore, the OLAFO incorporates contact plates with a rocker profile informed by roll-over-shape measurements to support forward progression and gait symmetry. Proof-of-concept biomechanical verification in one able-bodied participant evaluated complete offloading, five partial-loading levels, and normal gait using a pressure walkway to compute vertical ground reaction forces and impulses. In complete offloading, the affected foot generated no contact pressures. Across partial-loading levels, the foot impulse increased from 14% to 53% of the total load and scaled linearly with strut height adjustments, supporting clinician-prescribed loading increments. Contralateral stance duration increased only modestly compared to commonly used assistive devices, indicating reduced compensatory loading on the intact limb. These findings demonstrate the proof-of-concept feasibility of the OLAFO, highlighting its potential for verifying full offloading and prescribing partial-loading targets during rehabilitation. Future research will evaluate performance across patient populations and clinical rehabilitation tasks.

PurposeAnkle stiffness can be altered by normal aging, bone and joint pathology, and treatments such as orthoses or surgical joint fusion. The effects of ankle stiffness on gait are not yet well understood but may be crucial for understanding how these pathologies and treatments influence body mechanics. The objective of this work was to investigate how isolated changes in stiffness applied in parallel with the ankle impact lower-limb kinematics, kinetics, joint work, and muscle activation during walking in individuals without lower-limb pathology. MethodsNine young adults without lower-limb pathology wore an adjustable-stiffness ankle exoskeleton and walked at 31 different conditions of ankle spring stiffness, neutral angle, and treadmill incline. We recorded motion capture data, ground reaction forces, and muscle activation, and analyzed the resultant data for trends as a function of ankle stiffness. ResultsExoskeleton-side ankle range of motion decreased and asymmetry increased across all joints as ankle stiffness increased, primarily due to decreased plantarflexion at toe-off. The 30 Nm/rad spring stiffness condition led to a minimum in mean exoskeleton-side muscle activation and hip joint work, but increased kinematic asymmetry. ConclusionOur results suggest that there may exist a range of stiffnesses at the lower end of typically-studied values that can reduce muscle activation and joint work during walking, though at the cost of kinematic symmetry. These findings provide a deeper understanding of how ankle stiffness influences gait mechanics, with potential applications in wearable devices, clinical rehabilitation, and assistive technology.

Background and ObjectiveThe foot-ankle complex is a highly articulated and mechanically constrained system, often simplified as a chain of few rigid segments, neglecting many bone-to-bone motions and raising questions about the accurate representation of interaction with ground. This study proposes a new reduced-order multibody formulation that captures intrinsic kinematic constraints of the foot through motion synergies. MethodsBones kinematic coupling, or motion synergies, were experimentally derived from weight-bearing CT scans using principal component analysis. These couplings were embedded in a synergy-based multibody kinematic optimization framework describing the foot-ankle with five degrees of freedom: ankle flexion; foot adduction, pronation, and arching; and toe flexion. Model accuracy was evaluated against bone-level experimental kinematics. The model was applied to gait data from healthy, flat, and diabetic feet and compared with a standard multi-segment foot model, assessing robustness by progressively reducing the number of skin markers. ResultsAverage errors were about 1{degrees} and 0.5 mm when using subject-specific synergies and below 7{degrees} and 4 mm when scaling the generic model, matching or exceeding the accuracy of existing models. Reliable reconstruction was obtained using only four foot markers. In clinical gait analysis, the model showed superior discrimination between populations and enabled assessment of transverse arch deformation, not accessible with conventional models. ConclusionThe proposed synergy-based model provides an accurate, low-complexity framework for reconstructing bone-level foot and ankle kinematics, substantially simplifying gait analysis while improving biomechanical interpretability. This framework supports future integration with dynamic models aimed at studying load transmission in the foot.