Journal of Biomechanics

Importance: Foot drop impairs mobility for many children globally, causing life-long health issues. Existing treatments are costly, custom-made, and require frequent clinical visits. A new, low-cost, off-the-shelf splint (OrthoPed) could improve access and user experience. Objective: To determine the feasibility of recruiting children (4-17 years) with moderate foot drop and collecting biomechanical, clinical, and patient-reported outcomes to compare OrthoPed with existing treatments. Design: Single-centre cross-sectional feasibility and pilot study informing a future randomised clinical trial. Participants: Twelve children (target=20; mean age=10.6 {+/-} 3.5 years; 2 females) with moderate foot drop and prescribed orthotic support were recruited via physiotherapy. Intervention: The new OrthoPed splint was compared against existing treatments: ankle foot orthoses (AFOs) and Lycra socks. Main outcome measures: Primary outcome: recruitment and retention rates. Secondary outcomes: biomechanical and clinical gait measures, alongside useability and performance questionnaires. Results: Recruitment reached 22% of eligible participants (an "amber" rating for future trials). Despite four dropouts due to treatment burden, all outcome measures were successfully collected. Preliminarily, OrthoPed supported more natural gait mechanics than AFOs and offered better usability and comfort than AFOs and Lycra socks, potentially enhancing adherence. Conclusions: Recruiting children for orthotic trials is feasible, though coordinating gait testing with routine clinical appointments could improve future recruitment. Importantly, low-cost orthotic devices may provide better usability, accessibility and adherence than existing prescribed options.

BackgroundHuman gait is a key marker of motor development. While walking on even surface is well-documented, responses to irregular surfaces, closer to real-world environments, remain understudied. This limitation is reinforced by the frequent use of univariate analyses, though locomotor control emerges from interactions of multiple features. Multivariate approaches are therefore essential to characterize developmental modulations to uneven surfaces. Objectives(i) To evaluate the combined effects of age and surface complexity on multiple gait domains in healthy individuals during development; (ii) To identify locomotor profiles across gait maturation. MethodsSixty-eight participants (2-35 years) walked at a self-selected speed on even, medium, and high irregularity surfaces. Gait kinematics were captured using a 3D motion system. Linear Mixed Models evaluated age and surface effects on 28 variables across five domains: pace, rhythm, dynamic stability, variability, and asymmetry. Moreover, principal component analysis followed by k-means clustering was performed on 15 normalized variables to identify gait profiles. ResultsAge and surface influenced most variables (p<0.05). Young children (2-5 years) exhibited the greatest modulation of asymmetry, base of support, smoothness, and dynamic stability with surface complexity. Conversely, adults and adolescents (12-35 years) showed higher variability modulation on irregular surface. PCA-assisted clustering identified two clusters: Cluster1 (15.2 years, smooth-regular) and Cluster2 (6.1 years, wide-base-variable). Across surfaces, five subgroups emerged: two consistent (15.8 years in Cluster1; 5.4 years in Cluster2) and three switchers (8.5 years [7.6-12.8]) showing context-dependent transitions as surface complexity increased. DiscussionDifferential maturation and surface sensitivity suggest that irregular surfaces act as functional stressors, revealing developmental gaps hidden on even ground. The surface-dependent transition at 7-13 years suggests that locomotor maturity is task-dependent rather than a fixed state, shifting from stable, regular to a variability-driven, balance-supportive strategy with complexity. These profiles delineate developmental stages and may help to identify atypical trajectories in pediatric rehabilitation.

Ground reaction forces (GRFs) are important markers of gait impairment and rehabilitation, but direct measurement with force plates is expensive and limited to laboratory settings. OpenGRF is an open-source framework that estimates GRFs from motion-capture data using musculoskeletal modeling, yet its validity in pathological gait remains uncertain. This study evaluated the accuracy of OpenGRF for estimating three-dimensional GRFs during gait in healthy adults and in people with Parkinsons disease (PD), stroke, hip osteoarthritis (HOA), and total hip arthroplasty, and assessed its ability to reproduce clinically relevant GRF peak magnitudes and timings. OpenGRF estimates were compared with force-plate measurements in healthy participants and in cohorts with PD (OFF/ON medication), stroke, and HOA before surgery (M0) and 6 months after surgery (M6). Accuracy was quantified using root mean square error (RMSE), normalized RMSE (NRMSE), and Pearson correlation coefficients (PCC). Peak analyses examined biases in anterior-posterior (AP) braking and propulsive peaks, first and second vertical peaks (V1, V2), and the main mediolateral peak, as well as timing shifts across the gait cycle. One-dimensional statistical parametric mapping tested waveform differences (p = 0.01). OpenGRF reproduced overall GRF profiles across groups. Vertical GRF showed the best agreement (PCC 0.84-0.94; NRMSE 0.14-0.23), whereas mediolateral GRF showed low absolute error (RMSE 1.45-1.95 %BW) and moderate-to-good agreement (PCC 0.70-0.82). AP GRF was least accurate (PCC 0.61-0.73), especially during propulsion in PD (NRMSE up to 0.39). OpenGRF can reasonably estimate GRFs in healthy and pathological gait and may support kinetic gait assessment when force plates are unavailable.

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.

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.

Wearable exoskeletons are a promising tool for augmenting balance and reducing fall risk. Recent work suggests that active ankle exoskeletons need to act faster than the human to improve reactive balance control. However, the magnitude of exoskeleton torque that is best for improving reactive balance remains unknown. Drawing from the optimal torque for minimizing metabolic expenditure, we hypothesized that reactive balance would improve with increased exoskeleton torque. Participants wearing bilateral ankle exoskeletons were instructed to maintain standing balance during 15cm backward support-surface perturbations. Three exoskeleton plantarflexion torque conditions were tested: NO (Off), LOW (15Nm), or HIGH (30Nm). LOW torque improved balance performance compared to NO torque (p<0.001), with a 7{+/-}3% decrease in peak center of mass (CoM) displacement. Although HIGH torque caused a 9{+/-}11% decrease in peak CoM displacement compared to NO torque (p=0.12), it was not significant due to high intersubject variability. Whereas LOW torque decreased peak CoM displacement in all (range: -0.2 to -1.6cm), HIGH torque only decreased it in some (range = 1.2 to -2.6cm). The change in CoM displacement from LOW to HIGH torque was associated with balance ability, quantified by the narrowing beam test (R2=0.29, p=0.06), while this relationship didnt meet conventional statistical significance, likely due to the small sample size, it suggests that higher levels of exoskeleton torque may hinder balance performance in individuals with better balance ability. Taken together, more exoskeleton torque is not always better for balance, highlighting a potential need to personalize exoskeleton torque for balance augmentation.

This study presents the MMH dataset, a laboratory-collected in vivo dataset comprising whole-body kinematics, three-dimensional ground reaction forces and two-dimensional centres of pressure under both feet, as well as surface electromyography (sEMG) signals of twelve lower-limb muscles (six muscles per leg) during load lifting and lowering tasks. Ten healthy, normal-weight, young male adults each performed 72 trials combining one- and two-handed load (2 kg) lifting and lowering. These trials include multiple initial and final load locations while using three different lifting techniques (stoop, semi-squat, and full-squat). The kinematic and force-plate measurements provide rich input for ergonomic risk assessment tools and optimisation-based musculoskeletal models aimed at quantifying and managing musculoskeletal risk of injury. Also, the sEMG recordings enable the development of EMG-assisted musculoskeletal models and support validation of predictions from optimisation-based models. These makes the multimodal MMH dataset a valuable resource for biomechanics, ergonomics, and human movement research.

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.

Healthy individuals are hypothesized to adopt muscle coordination strategies that minimize energy expenditure. However, under pathological conditions, these patterns often change, as patients adopt alternative strategies to enhance stability. In musculoskeletal modeling, such changes in muscle coordination are often not accounted for when estimating internal quantities such as knee joint contact forces. To address this, we adapted the objective function of a muscle redundancy solver to inform muscle activations from electromyography measurements, minimizing errors in both muscle activations and co-contraction levels. The resulting estimates of knee joint contact force were compared with in vivo measurements across three activities. The co-contraction index-informed approach achieved the lowest root-mean-square-error of 0.31 body weight, averaged across all subjects and activities, improving the results of the minimum activation solver by 7% body weight. Notably, root-mean-squared-error increased with the level of co-contraction with the minimum activation approach ({beta} = 1.55, 95% confidence interval [0.79, 2.30]), whereas the co-contraction index-informed approach remained less sensitive ({beta} = 0.78, 95% confidence interval [0.14, 1.43]). These findings suggest that objective functions based on minimum muscle activation may be insufficient to accurately estimate knee joint forces in individuals with altered muscle coordination. Incorporating co-contraction information is therefore essential to capture subject-specific adaptations.

BackgroundThe pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL-deficient knees, yet grading remains subjective, as evidenced by poor inter-observer reliability, particularly for Grade 2. Since low-grade (Grade 1) versus high-grade (Grades 2/3) PS is the threshold for recommending lateral extra-articular augmentation, performing the test in awake clinic patients limits grading reproducibility and introduces variability in surgical decision-making. Existing methods to quantify the pivot shift usually require examiner-performed testing under general anaesthesia. No prior approach has ascertained PS grading from a separate patient-performed functional movement. PurposeTo evaluate the feasibility of a machine learning (ML) classifier, trained on kinematic ultrasound bone-tracking signals acquired during a patients sit-stand-sit (SSS) knee movement, to predict their PS grade, and to clinically validate its ability to differentiate low versus high-grade PS. MethodsUltrasound bone-tracking kinematic data were collected during SSS manoeuvres in 23 ACL-injured patients using the GATOR device, and ground truth PS grades (0-3) were assigned under general anaesthesia by fellowship-trained orthopaedic sports surgeons. From the data collected, Leave-one-out cross-validation (LOOCV) was used to train the ML classifier. Clinical SSS data from 6 ACL-deficient patients was used for independent held-out validation of their low-grade (Grade 1) versus high-grade (Grade 2/3) PS. Multiple deep learning architectures (XceptionTime, InceptionTime, FCN, ResNet, ResCNN) and training strategies (including mixup augmentation and supervised contrastive learning) were tested. Performance was measured by one-versus-rest (OVR) AUC under LOOCV and by AUC (low vs high grade PS) from the held-out patient sessions. ResultsThe ML classifier achieved a maximum OVR AUC of 0.928 {+/-} 0.084 under LOOCV. Classifier performance increased with pivot-shift severity: Grade 3 was identified most reliably (AUC ~0.81; sensitivity 0.70-0.80), whereas Grade 2 remained the most challenging boundary (sensitivity 0.20-0.75 across configurations). For the clinically relevant binary classification of low-versus high-grade pivot shift, the classifier generalised well to a completely unseen patient cohort (AUC 0.889; accuracy 0.860; sensitivity 0.850; minimum-class sensitivity 0.767). ConclusionThe study demonstrates that kinematic ultrasound bone-tracking during sit-stand-sit contains transferable information about rotational instability severity in ACL-deficient patients, and represents the first reported approach to predict pivot shift grade from a patient-performed functional movement. The strong cross-validation performance confirms that the signals contain meaningful PS grade-discriminative information, but larger datasets targeting 50-100 sessions per grade will be required to achieve patient-level generalisation and advance this novel rotational instability assessment tool toward full clinical adoption. Level of EvidenceLevel IV, diagnostic feasibility study.

Accurate estimation of body segment inertial properties is essential for biomechanical analyses, yet commonly used scaling methods rely on limited datasets and do not generalize well across diverse adult body morphologies. We developed a data-driven framework that estimates segment lengths, masses, centers of mass, and moments of inertia using regression models trained on large anthropometric datasets (ANSUR II and NHANES) combined with a geometric representation of 16 body segments. The framework uses height, weight, and sex as primary inputs and incorporates waist and hip circumferences or other length and cross-sectional measurements when available to refine body-shape predictions. For individuals with obesity, additional geometric rules redistribute excess mass based on segment-specific volume changes. The resulting models reproduced segment lengths, cross-sectional dimensions, and lumped segment masses within the ranges observed in the training datasets and outperformed published regression equations, particularly at higher body mass index (BMI) values. To promote broad adoption, we provide an open-source API in Python that performs the full parameter estimation using the trained models. This framework offers an accurate and accessible method for estimating adult body segment properties across a wide range of body sizes and shapes, supporting improved motion analysis, musculoskeletal simulation, and clinical biomechanics.

Objective: To identify brain injury metrics suitable for supporting sports head injury assessment by evaluating their association with brain tissue strain and consistency across sports. Methods: Head kinematics from 3,139 impacts in boxing, mixed martial arts, and rugby matches were recorded using instrumented mouthguards and used to calculate nine brain injury metrics. Impacts were simulated using an anatomically detailed finite element brain model to estimate peak 95th-percentile maximum principal strain (MPS) in the brain and brainstem, a measure of tissue deformation associated with long-term pathology. Sport-specific ordinary least squares models estimated xE, the metric value equivalent to a reference MPS of 0.21. Metric-MPS correlations and xE uncertainties were quantified using 5000 bootstrap resamples. Cross-sport consistency was assessed using the coefficient of variation (CV) of sport-specific median xE values, and uncertainty using the normalised confidence interval size (NCIS). Results: XGB, an extreme gradient boosting strain-prediction model, showed the strongest and most consistent correlations with whole-brain (r=0.924-0.974) and brainstem MPS (r=0.887-0.954) across all sports. PRV, BrIC and UBrIC also correlated strongly with whole-brain (r=0.724-0.930) and brainstem MPS (r=0.739-0.900), whereas HIC15 and HARM showed weaker correlation with MPS, particularly in rugby. XGB showed the lowest cross-sport variability (CV=0.034) and uncertainty (median NCIS=0.056). HARM, DAMAGE and HIC15 showed the greatest sport dependence (CV=0.575-0.588) and uncertainty (median NCIS=0.331-0.791). Conclusions: XGB, BrIC, and UBrIC demonstrated the strongest associations with brain tissue strain and the greatest consistency across sports. This study provides a biomechanically informed framework for selecting suitable metrics for sports HIA protocols.

Skeletal muscle is a living, perfused soft tissue whose viscoelastic behavior is shaped by both voluntary contraction and hemodynamic state. However, the independent and superimposed contributions of contractile loading and blood flow restriction (BFR) have not been quantified simultaneously in real time. Twenty-six healthy adults underwent multi-frequency ultrasound time-harmonic elastography (THE, 60-80 Hz) of the vastus lateralis under six conditions: rest, 15% and 30% maximal voluntary contraction (MVC) before BFR, passive BFR after 4 min of cuff inflation, and 15% and 30% MVC shortly after cuff release. Shear wave speed (SWS), reflecting elasticity, and penetration rate (PR), reflecting inverse viscous damping, were extracted using the k-MDEV inversion algorithm. BFR significantly elevated SWS at all three contraction levels relative to the corresponding pre-BFR measurements (Holm-corrected p [&le;] 0.011; dz = 0.54-2.13). PR decreased during resting BFR (dz = 1.34, p < 0.001) and at 15% MVC after cuff release (dz = 0.94, p < 0.001), but not at 30% MVC (dz = 0.21, p = 0.294). BFR-related changes reduced the SWS-force slope by 14.5% and the PR-force slope by 40.7%. Men exhibited a greater BFR-induced increase in resting SWS than women. These findings show that THE can distinguish contractile and hemodynamic contributions to skeletal-muscle viscoelasticity and provide complementary information on elastic and dissipative tissue behavior in vivo.

Background and PurposeBadminton imposes considerable cardiovascular and musculoskeletal stress. Physiological profiling can identify modifiable injury risk factors and inform exercise-based prevention and rehabilitation. This study compared cardiovascular recovery, neuromuscular activation, and limb strength between elite and recreational male university badminton players to derive preliminary physiological benchmarks for injury risk stratification and exercise rehabilitation guidance. MethodsForty male athletes (20 elite: national/university representatives with [&ge;]5 years of competitive experience; 20 recreational: <3 years of experience) completed assessments of heart rate recovery (HRR), biceps brachii surface electromyography (sEMG; SENIAM protocol), handgrip strength (JAMAR dynamometry), and maximal bodyweight squat repetitions. Independent-sample t-tests with Cohens d ( = 0.05) and Pearson correlations were applied. ResultsElite players demonstrated significantly greater handgrip strength (49.00{+/-}6.12 vs. 39.00{+/-}5.45 kg, p = 0.001, d = 1.72) and lower-limb (LL) strength (60.35{+/-}11.29 vs. 41.75{+/-}6.72 repetitions, p < 0.001, d = 1.96). Normalized sEMG root mean square (RMS) was higher in elite athletes during flexion (11.56{+/-}4.16% vs. 7.26{+/-}5.15%, p = 0.004, d = 0.94) and extension (12.67{+/-}4.56% vs. 7.85{+/-}5.73%, p = 0.003, d = 0.94). HRR did not differ significantly between groups (p = 0.17, d = 0.43, observed power = 0.34). Elite players nonetheless showed a more favorable recovery distribution. sEMG -HRR correlations were weak and non-significant in both groups. ConclusionsElite badminton players exhibit a distinct physiological profile of greater strength and more efficient neuromuscular activation. These preliminary cross-sectional findings may support the design of exercise-based injury-prevention and rehabilitation in university badminton.

We present three quantitative methods: 1) estimation of exoskeleton mechanical power and energy ratio from published data, 2) a systematic review of the exoskeleton literature on reported energy ratios, and 3) timing correction analysis of the replication experiment, to address concerns raised by Collins et al. (2026) about Luo et al. (2024). Together, these analyses support the reported metabolic reductions and the validity of exoskeleton control via learning in simulation. The critique rests on an unsupported premise: that exoskeleton energy ratios above 4 are physiologically implausible. This premise of Collins et al. (2026) is not supported by the cited evidence, and the error originates in their own cited source. Sawicki and Ferris (2009), the paper they invoke as authority for the limit of 4, state explicitly that "reported values of the muscular efficiency range from 0.10 to 0.34, with many sources assuming an average of [~]0.25." The value of 4 corresponds to this average, it is not a physiological ceiling. Treating an average as a physiological upper limit is a fundamental error. The published exoskeleton literature further contradicts the claim, including work by the authors of the critique themselves (Collins et al., 2015: 4.3; Young et al., 2017: 5.0) and independent work (Malcolm et al., 2013: 4.8; Seo et al., 2017: 6.7). In contrast, our walking energy ratio is 2.4, calculated directly from Fig. 4 of our paper. Our device delivers higher peak torque (14.1 Nm vs. 10.9 Nm, Lim et al., 2019) and achieves a slightly larger metabolic reduction (24.3% vs. 21%). Independent groups have since demonstrated meaningful metabolic reductions using learning-in-simulation frameworks, including Barati et al. (2026, 15.2% mean and 22.5% maximum) and Zhou et al. (2025, [~]20% during running). The claim of Collins et al. (2026) that this problem "remains unsolved" is directly contradicted by these independent results. The experiment in the critique is not a valid replication of our method. Our controller is a neural network with [~]10,000 parameters learned through deep reinforcement learning in musculoskeletal simulation; the critique instead applies a pre-programmed fixed torque curve with no learnable parameters. Beyond this, the replication contains three methodological errors: 1) a heel-strike timing assumption producing offsets up to 30% of the gait cycle; 2) an averaged torque profile that discards subject-specific control; and 3) a device [~]50% heavier than ours (4.8 kg vs. 3.2 kg) without measuring the metabolic penalty of the added weight. The critique also misreports Samsung data, with reported values approximately double those in the original publication, errors that directly underpin their physiological limit argument.

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.

This cross-sectional derivation and internal validation study aimed to develop and internally validate a clinical triage scoring system (CTSS) for field-based identification of collegiate athletes requiring priority intervention for lumbopelvic-hip (LPH) dysfunction. A total of 864 collegiate athletes (mean age 21.3 {+/-} 2.4 years; 80.8% male) were recruited from 10 universities. Participants underwent standardized assessments including demographic characteristics, clinical history, and LPH functional testing. Using an expert-adjudicated binary reference standard (priority intervention vs self-management), a multivariable logistic regression model was developed to derive the weighted CTSS. Model performance was evaluated using discrimination, calibration, and decision curve analysis (DCA), and internal validation was performed using 1,000 bootstrap resamples. Of the 864 participants, 463 athletes (53.6%) were classified as requiring priority intervention. The final 14-factor CTSS comprised 12 positive-weight predictors, such as localized LPH pain, muscle weakness, and higher body mass index, and 2 negative-weight predictors, positive Lasegues sign and hamstring weakness, which functioned served as safety-related modifiers. The model demonstrated acceptable discrimination (AUROC = 0.851, 95% CI: 0.824-0.876), with minimal optimism (optimism-corrected AUROC = 0.842) and excellent calibration (calibration slope = 1.000; calibration intercept = 0.000). A total score of [&ge;]9 was identified as the optimal threshold, yielding a sensitivity of 84.4% and specificity of 71.8%. DCA showed greater net benefit than treat-all and treat-none strategies across clinically relevant threshold probabilities (20%-50%), with a net benefit of 0.319 at a 50% threshold probability. The CTSS may provide a pragmatic field-based triage tool to support early identification of athletes who may require priority intervention, although external validation is needed before broader implementation in sports medicine settings.

ObjectivesTo determine whether early functional severity at presentation explains variability in return to sport (RTS) after ankle sprain in young athletes, compared with sprain subtype and injury mechanism. DesignRetrospective cohort study. MethodsAthletes aged [&le;]22 years with acute ankle sprains were identified from a prospectively maintained institutional database. Surgically treated cases were excluded. Functional severity at presentation was classified into three grades based on the ability to continue sports participation and ambulate immediately after injury. Injury mechanisms were categorized as high-energy deceleration (HED) or non-HED. RTS was analyzed as time to return and as prolonged RTS ([&ge;]4 weeks). Multivariable logistic regression was performed to identify factors independently associated with prolonged RTS. ResultsA total of 437 cases were included. Median RTS was 2.0 weeks (interquartile range, 0.0-4.0), and prolonged RTS occurred in 33.0% of cases. RTS duration increased stepwise with greater functional severity (p < 0.001). In multivariable analysis, functional severity was strongly associated with prolonged RTS (Grade 2: adjusted odds ratio [OR], 3.58; 95% confidence interval [CI], 2.07-6.19; Grade 3: adjusted OR, 24.53; 95% CI, 10.67-56.43; p < 0.001), and age was also independently associated (adjusted OR, 1.19 per year; 95% CI, 1.11-1.27; p < 0.001). Sprain subtype and injury mechanism were not independently associated with RTS after adjustment. ConclusionsEarly functional severity at presentation is the primary determinant of RTS after ankle sprain in young athletes. Apparent differences related to sprain subtype and injury mechanism are largely explained by initial functional impairment.

BackgroundThe clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability. ObjectiveThe purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction. MethodsThis prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed [&ge;]6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8 ACL-deficient, 21 ACL-reconstructed [&ge;]6 months post-surgery) were recruited through physician referral at SGH outpatient clinics to perform standardized chair-stand tests. An ensemble deep learning model that combines convolutional (EfficientNet) and time-series (InceptionTime) classifiers is used to output binary stability classifications (ACL-deficient/ACL-reconstructed). The models performance was evaluated using 10-fold stratified cross-validation with patient-wise splitting, repeated across 100 random seeds to assess variability. ResultsAt the halfway point of the trial, the ensemble model performance with regard to the Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.8365 (SD: 0.042, p-value < 0.001), and the classification accuracy was 75.9% (SD: 3.2%) when the model was tested on the 29 CIRB-approved patients. For the ACL-reconstructed class, the performance indicators were as follows: precision 71.4%, recall 93.8%, F1-score 81.1%. For the ACL-deficient class, the indicators were: precision 87.5%, recall 53.8%, F1-score 66.7%.Against the clinical pivot shift tests low sensitivity (24-32%), the model delivers an almost 2X better sensitivity (53.8%)[2, 3], with a comparable specificity (93.8% vs. 90-98%) ConclusionThe multimodal machine learning model was able to perform at a level that was relevant to clinical classification (AUC-ROC 0.8365, accuracy 75.9%) in differentiating between ACL-deficient and ACL-reconstructed knees. Moreover, the model demonstrated far superior sensitivity than previously published estimates for manual pivot shift testing (53.8% vs. 24-32%). These findings demonstrate that rotational knee instability can be reliably differentiated in clinical settings with a ML model deployed on GATOR PRO data.

Background: Quadriceps strength and landing mechanics are two modifiable factors associated with anterior cruciate ligament (ACL) injury risk. Collecting detailed biomechanical data is an arduous task. Identifying a relationship using more easily measured variables, such as quadriceps strength, would offer value for athlete counseling and injury prevention programs. Although quadriceps weakness has been associated with altered landing strategies in ACL-reconstructed (ACLR) individuals, this relationship is less clear in healthy athletes. Purpose: To investigate the association between isokinetic quadriceps strength and peak knee flexion angle during a vertical drop jump in healthy adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes had their dominant leg quadriceps strength measured using an isokinetic dynamometer at 60{degrees}/s from 0-90{degrees} of knee flexion. Landing mechanics were assessed during a vertical drop jump using three-dimensional motion capture synchronized with force plates. Pearson correlation was used to evaluate the association between quadriceps strength and peak knee flexion angle during landing, with statistical significance defined as p < .05. Results: There was a weak negative correlation between quadriceps strength and peak knee flexion angle (p = .017, R = -.22 [-.04, -.38]), suggesting that stronger athletes achieved greater knee flexion angles. Discussion: Greater quadriceps strength was associated with increased peak knee flexion angles during landing; however, the weak correlation suggests that strength explains only a small portion of the variability in landing mechanics. These findings deviate slightly from prior literature in healthy populations but are consistent with studies demonstrating that greater quadriceps strength is associated with achieving greater peak knee flexion in ACLR patients. Accordingly, quadriceps strengthening should remain a key component of multifactorial ACL injury prevention programs.