Scott L. Delp

Background: Chronic pain is associated with impaired muscle health, but whether these changes reflect site-specific factors, broader systemic factors, or both remains unclear. The purpose of this study is to determine whether normative markers of muscle health derived from MRI show site-specific patterns in chronic pain. Methods: UK Biobank participants who underwent whole-body MRI from 2006 to 2010 were included in this retrospective cross-sectional study. The MuscleMap Toolbox quantified volume and intramuscular fat (IMF) in 42 muscles of the abdomen, pelvis, and thigh. Normative models trained on a no pain group generated muscle-specific deviations from normal (i.e., Z-scores) for single- and multi-site chronic and acute pain. Results: Of 17,843 participants, the primary site-specific analysis included 9,704 no pain, 885 single-site chronic back pain (CBP), 438 single-site chronic hip pain (CHP), and 1,315 single-site chronic knee pain (CKP) participants (n=12,342; mean age 63.7±7.5 years; 52.7% female). Additional analyses included single-site chronic neck/shoulder pain, acute pain, and multi-site chronic pain groups. In CBP, deviations were localized to abdominal muscles, with decreased volume in 6/8 and increased IMF in 6/8. In CHP, deviations were broad, with decreased volume in 3/8 of the abdominal and 14/26 of the thigh muscles, and increased IMF in 6/8 of the abdominal, 5/8 of the pelvic, and 4/26 of the thigh muscles. In CKP, deviations were localized to thigh muscles, with decreased volume in 8/26 and increased IMF in 6/26. Acute pain groups showed no significant differences except for decreased volume in one thigh muscle in acute knee pain. With each additional chronic pain site, volume decreased (β=-.078;IQR:-0.100-0.051), and IMF increased (β=.085;IQR:0.066-0.101). Combined Z-scores classified chronic pain groups better than chance (accuracy: 48.6%;p<.001), but not acute pain groups (accuracy: 39.0%;p=.20). Conclusions: Whole-body MRI combined with AI-driven muscle segmentation and normative modeling revealed site-specific patterns of muscle health in single-site chronic pain.

Objectives: Knee osteoarthritis (KOA) is a leading cause of disability, yet which patients will experience structural decline remains unclear. Body mass index (BMI) and lower limb alignment are established risk factors for KOA, but their independent and interactive effects on compartment-specific cartilage loss and total knee replacement (TKR) have not been characterized at scale. Methods: We analyzed 5,832 limbs from 3,016 participants in the Osteoarthritis Initiative followed over 7 years. Cartilage thickness in the weight-bearing medial and lateral femur and tibia was quantified, and lower limb alignment was measured using hip-knee-ankle (HKA) angle obtained from full-limb radiographs. Linear mixed-effects models estimated the independent and interactive effects of BMI and lower limb alignment on longitudinal cartilage thinning, and mixed-effects logistic regression modeled TKR risk. Results: BMI and +10° varus, the rate of medial femur cartilage thinning was 243.5% faster than the reference rate. In the lateral compartment, BMI and valgus alignment were independently associated with faster cartilage thinning, with no significant interaction. TKR risk increased exponentially with HKA deviation (odds ratio [OR] = 1.38 per 1°; ~five-fold at 5° malalignment) but was not associated with BMI. Conclusion: BMI and lower limb alignment influence structural KOA progression through compartment-specific pathways. The multiplicative interaction in the medial compartment identifies high BMI combined with varus malalignment as a discrete high-risk phenotype, with implications for clinical risk stratification and disease-modifying intervention design.

This entry presents multi-society-endorsed guidelines for sharing human movement data, as well as an overview of the relevant ethical, legal, and metadata considerations. The main manuscript is presented alongside a proposed international informed consent form template (S1), additional infrastructure considerations (S2), and a practical open data sharing checklist (S3). The supplementary materials S1 and S3 are intended as living documents that can evolve alongside changes in legislation. Input from the community is welcome to help guide future revisions and the inclusion of additional jurisdictions not yet represented.

Objective: To identify which immediate changes in knee loading from a gait modification relate to changes in cartilage microstructure after one year. Method: This was a one-year retrospective observational cohort study of twenty-five participants with medial knee osteoarthritis who walked with a personalized gait modification that reduced their peak knee adduction moment by modifying their foot progression angle. Musculoskeletal modeling was used to estimate knee loading changes in response to the gait modification at the initial timepoint. Magnetic resonance imaging (MRI) was conducted at baseline and after one year of gait modification to assess the change in T1ρ and T2 relaxation times in the medial weight-bearing compartment of the knee. Canonical correlation analysis (CCA) was performed to find relationships between measures of knee loading and cartilage microstructure. Results: CCA revealed that changes in cartilage microstructure were significantly correlated to knee loading metrics (r=0.84, 95% CI: 0.66–0.93, p<0.0001). Reductions in peak anteroposterior shear force (r=0.43, 95% CI: 0.05–0.71, p=0.03) and vertical force (r=0.42, 95% CI: 0.03–0.70, p=0.04) in the medial compartment during late-stance relate to reductions in the T2 relaxation time in the medial weight-bearing femoral cartilage. Conclusions: Baseline and immediate changes in knee loading metrics during the initial adoption of a gait modification are associated with improvements in MRI-estimated measures of cartilage microstructure after one year of gait retraining. Knee contact force metrics may be more associated with cartilage health than knee moments, which currently serve as biomechanical targets for interventions. Compressive and shear forces should be explored as targets for load-reducing interventions for medial knee osteoarthritis.

Abstract An exotendon—a spring that couples the dynamics of the legs when attached to a runner’s shoes—reduces the energetic cost of running, but the effects on joint contact forces are unknown. This study examined whether running with an exotendon alters the forces in the hip, knee and ankle. We used muscle-driven simulations of experimental data to compute compressive and shear contact forces at the hip, knee, and ankle joints for five participants running at 2.7 m/s with and without an exotendon. We found that runners using the exotendon experienced a 9.4% reduction in peak knee compressive contact force (1.0 ± 0.6 BW; P =0.036), and no change in the peak knee shear contact force. The primary contributor to this reduction was lower forces in the quadriceps muscles, which decreased their contribution to peak knee compressive contact force by 14.2% (-0.9 ± 0.6 BW; P=0.026). We observed no change in the peak compressive or shear contact forces in the hip or ankle joints. Though the exotendon was not originally designed to reduce joint forces, our findings highlight the ability of this simple device to make changes to gait that reduce both energetic cost and compressive knee force.

Recent advances in text-to-image diffusion models have been driven by the increasing availability of paired 2D data. However, the development of 3D diffusion models has been hindered by the scarcity of high-quality 3D data, resulting in less competitive performance compared to their 2D counterparts. To address this challenge, we propose repurposing pre-trained 2D diffusion models for 3D object generation. We introduce Gaussian Atlas, a novel representation that utilizes dense 2D grids, enabling the fine-tuning of 2D diffusion models to generate 3D Gaussians. Our approach demonstrates successful transfer learning from a pre-trained 2D diffusion model to a 2D manifold flattened from 3D structures. To support model training, we compile GaussianVerse, a large-scale dataset comprising 205K high-quality 3D Gaussian fittings of various 3D objects. Our experimental results show that text-to-image diffusion models can be effectively adapted for 3D content generation, bridging the gap between 2D and 3D modeling.

Digital health tools provide longitudinal physiological and behavioural data that can address knowledge gaps in women's health. This is particularly relevant for understanding hormone-driven physiological changes and symptoms, which impact health and performance across the lifespan. We conducted a scoping review of research using wearables or smartphone applications to identify insights about physiology, health behaviours, and symptoms throughout the menstrual cycle and menopausal transition. We identified 40 original articles. We summarise findings that reproduce lab-based results, giving confidence in the use of digital health tools for studying menstrual health, along with new insights gained. Given the importance of validation against gold standards, and the lack of a prior synthesis of wearable accuracy for women's health applications, we next report accuracies of wearables that measure biometrics relevant to menstrual health. Finally, we discuss future research needs, including understanding physiological changes during perimenopause, and the role of health behaviours in symptom management.

Motivation: Hand function is impaired in many conditions. MRI-derived muscle health markers may improve the evaluation of conditions affecting hand function. Traditional manual segmentation is time-consuming, necessitating automated approaches. Goal(s): Develop an accurate method to automatically assess forearm muscle health (muscle volume, intramuscular fat) and assess their relationship to hand function. Approach: We developed and tested a computer-vision model for automated forearm segmentation using fat-water MRI, then assessed associations between muscle health (volume, intramuscular fat) and hand function (grip strength, dexterity). Results: The computer-vision model achieved high accuracy and good-excellent reliability. Muscle volume was associated with BMI and grip strength. Impact: We developed an accurate, reliable computer-vision model to automatically segment forearm muscles, which will be made openly available. This method can improve clinical assessment of forearm muscle health leading to more efficient evaluation and management of conditions affecting hand function.

Motivation: Orthopaedic Digital Twins can compute tissue mechanics, with the potential to inform diagnoses and interventions. However, orthopaedic Digital Twins are not implemented clinically because the creation process is time-consuming and prone to error. Goal(s): Develop an automated Digital Twin pipeline and quantify how individual joint geometry affects knee cartilage pressures during gait. Approach: We developed a novel Neural Shape Model-based pipeline to create personalized Digital Twins for 150 subjects from the Osteoarthritis Initiative. Results: Automated Digital Twins showed: 1) regions of high cartilage pressure undergo the greatest cartilage thinning in osteoarthritis. 2) Osteoarthritis joint shape increases cartilage contact areas, thus decreasing cartilage pressure. Impact: Our fully automated Digital Twin estimates cartilage pressures during gait that relate to future cartilage thinning, and osteoarthritis progression. These findings indicate that Digital Twins have the potential to be implemented clinically, and hold promise for understanding and treating osteoarthritis.

Motivation: Evaluate long-term muscle adaptations across the full volume of all four hamstrings in response to Nordic hamstring exercise (NHE) to enhance injury prevention strategies. Goal(s): Examine how 9-weeks of supervised NHE-training affects architecture (volume, fiber length, angle, and curvature) and microstructure (MD, RD, FA, and T2) of Biceps femoris short head (BFsh), Biceps femoris long head (BFlh), Semitendinosus (ST), and Semimembranosus (SM). Approach: 11 subjects underwent MRI scans (Dixon, DTI, and T2) pre and post 9-weeks NHE-training. Results: NHE-training increased hamstring volume with greater hypertrophy in ST and BFsh muscles. Hypertrophy was accompanied by increases in both length and cross-section of muscle fibers. Impact: This study examines architectural and microstructural adaptations of the hamstrings following 9-weeks of Nordic hamstring exercise training. Findings reveal significant, but non-uniform hypertrophy among hamstrings accompanied by increase in length and size of the muscle fibers, advancing injury prevention strategies.

Motivation: Hand function can be disrupted by injury to the brain, SC, and peripheral nerves leading to weakness and impaired coordination. With simultaneous brain-spinal cord (SC) fMRI, we can non-invasively assess the neural mechanisms underlying motor control of the hand. Goal(s): Map brain and SC activity to improve our understanding of the neural correlates of hand function. Approach: Simultaneous brain-SC fMRI was performed on twenty-eight healthy volunteers during force matching and finger tapping experiments with three task levels. Results: Graded brain and SC activity across the task levels was identified, including activation and deactivation of sensory and motor regions. Impact: We mapped brain and SC neural correlates of hand function showing activations and deactivations in sensory and motor regions, providing a more complete picture of motor control.

The impact of testosterone therapy on hormone cycles, exercise capacity, and physiology of transmasculine individuals is not well understood. Existing studies report limited metrics at large time intervals between data collection. Here, we collect high-resolution temporal data from a single amateur trans male triathlete over a time period of 13 months–including one baseline month and twelve months on testosterone therapy–to characterize hormone, strength, body composition, aerobic, and training load profiles. Daily urine hormone monitoring revealed that progesterone and luteinizing hormone are the clearest metrics to predict cessation of menses. After one year, the participant increased lean body mass by 12%, average hand grip strength by 13%, jump height by 16%, and average knee isometric strength by 15%, but, in contrast with prior research, did not lose fat mass or show changes in isokinetic knee strength. While absolute VO 2 max increased by 10%, relative VO 2 max (e.g., normalized to body mass) only increased during corresponding peaks in training load. We provide guidelines to monitor trans males during testosterone therapy and recommendations to scale this case study to a larger population.

BACKGROUND: Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread types of assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers, but it is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable the quantification of three-dimensional movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested. METHODS: To compare video-based analysis with TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings, we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-times sit-to-stand) while capturing additional disease-specific signatures of movement. RESULTS: Video-based biomechanical analysis reproduced all four TFTs (r>0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (P=0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement, such as differences in gait kinematics, that are not evident in TFTs. CONCLUSIONS: Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions. (Funded by the Wu Tsai Human Performance Alliance and others.).

Abstract Background Sprinting and Nordic hamstring exercise (NHE) programs are common training modalities used to reduce hamstring injury risk, but the differences in the biomechanical demands of sprinting and the NHE are unclear. The purpose of this study was to compare knee and hip joint kinematics and kinetics, and hamstrings muscle-tendon unit (MTU) length and velocity during the flight phase of sprinting and the NHE. Methods We collected motion capture and force data from fourteen young athletic participants (8 males and 6 females) as they ran at a range of speeds (4–8 m/s) and performed the NHE. We used this experimental data and a musculoskeletal model to compute joint angles, moments, work, and power and to estimate the hamstrings MTU length and velocity for all running speeds and the NHE. Results The peak knee flexion moment at running speeds of 6 m/s and above was greater than for the NHE ( p &lt; 0.001). Peak negative knee flexion power at all running speeds was higher than during the NHE ( p &lt; 0.001). Negative knee flexion work at running speeds of 6 m/s and slower was less than during the NHE ( p &lt; 0.001). Peak hamstrings length and lengthening velocity were greater ( p &lt; 0.001) for all running speeds compared to the NHE. Conclusion Sprinting puts the hamstrings at longer hamstrings lengths and higher hamstrings lengthening velocities than the NHE. The NHE requires participants to generate peak knee flexion moments that are smaller than the peak knee flexion moments generated during top speed sprinting and peak negative knee flexion powers that are less than 5% of sprinting. However, the duration of each NHE repetition is approximately 60 times longer than the hamstrings lengthening portion of the flight phase of running, resulting in comparable negative knee work. The results of this study provide necessary quantitative information to compare the biomechanical demands of sprinting and the NHE.

Abstract While physical activity is critical to human health, most people do not meet recommended guidelines 1,2 . Built environments that are more walkable have the potential to increase activity across the population 3–8 . However, previous studies on the built environment and physical activity have led to mixed findings, possibly due to methodological limitations such as small cohorts, over-reliance on self-reported measures and cross-sectional designs 5,7,9–11 . Here we address these limitations by leveraging a large US cohort of smartphone users ( N = 2,112,288) to evaluate within-person longitudinal behaviour changes that occurred over 248,266 days of objectively measured physical activity across 7,447 relocations among 1,609 US cities. By analysing the results of this natural experiment, which exposed individuals to differing built environments, we find that increases (decreases) in walkability are associated with significant increases (decreases) in physical activity after relocation. For example, moving from a less walkable (25th percentile) city to a more walkable city (75th percentile) increased walking by 1,100 daily steps, on average. These changes hold across different genders, ages and body mass index values, and are sustained over 3 months. The added activity is predominantly composed of moderate-to-vigorous physical activity, which is linked to an array of associated health benefits 1 . Evidence against residential self-selection confounding is reported. Our findings provide robust evidence supporting the importance of the built environment in directly improving health-enhancing physical activity and offer potential guidance for public policy activities in this area.

BACKGROUND Retraining individuals with medial compartment knee osteoarthritis to walk with a patient-specific change in their foot angle (ie, toe-in or toe-out angle) can reduce excessive joint loading related to disease progression. This study investigated the clinical, biomechanical, and structural efficacy of personalised foot progression angle modifications compared with sham treatment in patients with mild-to-moderate medial compartment knee osteoarthritis. METHODS In this single-center, parallel-group, randomised controlled trial, we recruited individuals with symptomatic medial compartment knee osteoarthritis at the Human Performance Laboratory and Lucas Center for Imaging at Stanford University, CA, USA, using online and print media. Eligible participants (aged ≥18 years) were randomly assigned (1:1) by a computer to an intervention or sham group. During six walking retraining visits to a university gait laboratory, all participants received real-time biofeedback instructing them to walk consistently with a personalised target foot progression angle. The intervention group's target was the 5° or 10° change in foot progression angle that maximally reduced their knee loading, and the sham group's target was their natural foot progression angle. Participants and staff involved in data analysis were masked to group allocation; staff performing the gait analysis visits were not. Primary outcomes were 1-year changes in medial knee pain (numeric rating scale) and medial knee loading (knee adduction moment peak). Secondary outcomes were 1-year changes in cartilage microstructure estimated from MRI (T1ρ and T2 relaxation times). We evaluated safety by monitoring the number and type of adverse events. Intention-to-treat linear regression analyses, comprising all randomly assigned participants, were conducted. People with lived experience of knee osteoarthritis were involved in the design and conduct of this study. This study is registered with ClinicalTrials.gov, NCT02767570, and is closed to enrollment. FINDINGS Between Aug 1, 2016, and June 25, 2019, 1582 individuals were screened for eligibility. 107 participants completed an initial gait analysis and 68 were randomly assigned to either the intervention (n=34) or the sham (n=34) group. 41 (60%) of 68 participants were female, 27 (40%) were male, and 54 (79%) were White; mean age was 64·4 years (SD 7·6). After 1 year, participants in the intervention group had greater reductions in medial knee pain (between-group difference -1·2, 95% CI -1·9 to -0·5; p=0·0013) and knee adduction moment peak (between-group difference -0·26 % bodyweight × height, 95% CI -0·39 to -0·13; p=0·0001) than participants in the sham group. The MRI-estimated change in cartilage microstructure (T1ρ) in the medial compartment was less in the intervention group than the sham group (between-group difference -3·74 ms, 95% CI -6·42 to -1·05). There were no significant between-group differences in T2. There were no severe adverse events; however, two (6%) of 34 participants in the intervention group and one (3%) of 34 participants in the sham group dropped out of the study due to increased knee pain. INTERPRETATION Personalised foot angle modifications improve pain, reduce knee loading, and might slow osteoarthritis progression, making them a promising non-surgical treatment option for some individuals with medial compartment knee osteoarthritis. FUNDING US Department of Veterans Affairs.

ABSTRACT Muscle strength declines with aging at a faster rate compared with muscle mass, suggesting that not only muscle quantity but also muscle quality and architecture are age‐dependent. This study tested the hypothesis that quantitative MRI (qMRI)‐derived biomarkers of muscle quality (fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [ AD] , fat fraction [FF], and T 2 relaxation time) and architecture (fascicle length) could improve the prediction of skeletal muscle strength over muscle mass alone. We recruited 24 adults (12 female, age range 30–79 years). Muscle mass was estimated as the volume and cross‐sectional area (CSA) of the quadriceps. FA, RD, and AD parameters, together with fascicle length for the rectus femoris (RF) and vastus lateralis (VL), were derived from diffusion tensor imaging (DTI), and muscle‐T 2 was calculated from a multi‐echo spin echo sequence. FF was determined using the Dixon approach. CSA values were combined with FF to calculate the lean CSA. Isometric, eccentric, and concentric knee extension torques were measured for the left and right leg using an isokinetic dynamometer. The univariable assessment of torque was performed using a linear regression. The statistical significance of adding qMRI parameters to the torque prediction models was tested using a mixed‐effect regression. The best univariable predictor of isometric, eccentric, and concentric torque was lean CSA. Adding FA, RF fascicle length, and VL fascicle length to the model improved the prediction of concentric torque compared with CSA alone. The addition of FA, T 2 , RD, RF fascicle length, and VL fascicle length improved the prediction of eccentric torque over CSA alone. The addition of FF was not significant within the model. Our results confirmed the hypothesis that the inclusion of qMRI parameters of muscle composition and architecture leads to higher R 2 coefficients for the prediction of muscle strength compared with models solely based on muscle quantity. These observations support the utility of qMRI for future research on sarcopenia prediction and management.

INTRODUCTION: Sprint performance is a priority for coaches and athletes. Several kinematic variables, including horizontal touchdown distance (HTD) and inter-knee touchdown distance (IKTD), are targeted by coaches to increase top sprinting speed. However, the results of past research are conflicting, potentially due to the use of experimental inter-athlete study designs where it is not possible to establish cause-effect relationships. METHODS: In this study, we used a predictive simulation approach to assess cause-effect relationships between HTD and IKTD and sprinting speed. We scaled a three-dimensional musculoskeletal model to match the anthropometry of an international caliber male sprinter and generated predictive simulations of a single symmetric step of top-speed sprinting using a direct collocation optimal control framework. We first used our simulation framework to establish the model's top speed with minimal constraints on touchdown kinematics (the optimal simulation). Then, in additional simulations, we enforced specific HTD or IKTD values (±2, 4, and 6 cm compared with optimal). RESULTS: The model achieved a top speed of 11.85 m·s -1 in the optimal simulation. Shortening HTD by 6 cm reduced speed by 7.3%, whereas lengthening HTD by 6 cm had a smaller impact on speed, with a 1.6% reduction. Speed in the simulation was insensitive to the IKTD changes we tested. CONCLUSIONS: The results of our simulations indicate that there is an optimal HTD to maximize sprinting speed, providing support for coaches and athletes to adjust this technique variable. Conversely, our results do not provide evidence to support utilizing IKTD as a key technique variable for speed enhancement. We share the simulation framework so researchers can explore the effects of additional modifications on sprinting performance ( https://github.com/nicos1993/Pred_Sim_Sprinting ).

ABSTRACT Background Hand function is critical for daily activities and declines early in many diseases, conditions or disorders affecting the musculoskeletal and neurologic systems. Muscle health markers derived from clinically available magnetic resonance imaging (MRI) scans are strongly associated with functional capacity, may enhance clinical assessment and inform management options. However, traditional muscle MRI assessments require time‐intensive manual segmentations. Here, we aim to develop and test a computer‐vision model for automated forearm muscle segmentation and investigate associations between MRI‐derived muscle markers and age, sex, BMI, functional grip strength and dexterity measures. Methods We recruited 42 healthy, right‐handed adults (54.8% female, median age 37.3 years, median BMI: 23.0). Grip strength and dexterity were measured using the NIH Toolbox motor battery. Dixon fat‐water MRI of the right forearm was acquired at 3.0 T, and forearm flexor and extensor muscle compartments were manually segmented for model training. A 2D U‐Net convolutional neural network model was trained and tested for segmentation of the forearm flexors and extensors for the assessment of muscle volume and intramuscular fat. Testing accuracy and reliability were assessed using Sørensen–Dice indices, intraclass correlation coefficients (ICCs) and Bland–Altman analyses. Associations between the MRI‐derived muscle markers, demographic factors, muscle metrics and hand function were evaluated using partial correlations and regression models. Results The segmentation model showed high test accuracy, achieving mean Sørensen–Dice indices of 0.89 (flexors) and 0.85 (extensors) and ICCs of 0.75–0.99 for muscle volume and intramuscular fat. Muscle volume was positively correlated with BMI ( p &lt; 0.001) but not age ( p &gt; 0.249). Males had larger muscle volumes than females ( p &lt; 0.001), with no sex differences in intramuscular fat ( p &gt; 0.141), and no association between intramuscular fat and grip strength or dexterity ( p &gt; 0.350). We observed strong positive correlations between grip strength and both flexor ( p = 0.004) and extensor ( p = 0.001) muscle volumes, while dexterity showed no significant associations. Conclusions Our findings highlight the accuracy and reliability of automated forearm muscle segmentation using computer vision. BMI emerged as a key determinant of muscle volume, independent of age. The strong association between muscle volume and grip strength demonstrates the clinical relevance of these metrics, suggesting potential applications in therapeutic planning for conditions impairing hand function. Sex‐based differences in muscle volume underscore the importance of tailored assessments. Computer vision models integrated with Dixon fat‐water MRI enable efficient, accurate evaluation of forearm muscle health. Future research should explore these metrics in clinical populations and their utility in tracking functional outcomes.

BACKGROUND: While Nordic hamstring exercise (NHE) training has been shown to reduce hamstring strains, the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear. This study investigates architectural and microstructural adaptations of the biceps femoris short head (BFsh), biceps femoris long head (BFlh), semitendinosus (ST), and semimembranosus (SM) in response to an NHE intervention. METHODS: Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining. Magnetic resonance imaging was performed at pre-training, post-training, and detraining to assess architectural (volume, fiber tract length, and fiber tract angle) and microstructural (axial (AD), mean (MD), radial (RD) diffusivities, and fractional anisotropy (FA)) parameters of the 4 hamstrings. RESULTS: NHE training induced significant but non-uniform hamstring muscle hypertrophy (BFsh: 22%, BFlh: 9%, ST: 26%, SM: 6%) and fiber tract length increase (BFsh: 11%, BFlh: 7%, ST: 18%, SM: 10%). AD (5%), MD (4%), and RD (5%) showed significant increases, but fiber tract angle and FA remained unchanged. After detraining, only ST showed a significant reduction (8%) in volume, which remained higher than the pre-training value. While fiber tract lengths returned to baseline, AD, MD, and RD remained higher than pre-training levels for all hamstrings. CONCLUSION: The 9-week NHE training substantially increased hamstring muscle volume with greater hypertrophy in ST and BFsh. Hypertrophy was accompanied by increases in fiber tract lengths and cross-sections (increased RD). After 3 weeks of detraining, fiber tract length gains across all hamstrings declined, emphasizing the importance of sustained training to maintain all the protective adaptations.

Measuring balance is important for detecting impairments and developing interventions to prevent falls, but there is no consensus on which method is most effective. Many balance metrics derived from steady-state walking data have been proposed, such as step-width variability, step-time variability, foot placement predictability, maximum Lyapunov exponent and margin of stability. Recently, perturbation-based metrics such as center of mass displacement have also been explored. Perturbations typically involve unexpected disturbances applied to the subject. In this study we collected walking data from 10 healthy human subjects while walking normally and while impairing balance with ankle braces, eye-blocking masks and pneumatic jets on their legs. In some walking trials we also applied mechanical perturbations to the pelvis. We obtained a comprehensive biomechanics dataset and compared the ability of various metrics to detect impaired balance using steady-state walking and perturbation recovery data. We also compared metric performance using thresholds informed by data from multiple subjects versus subject-specific thresholds. We found that step-width variability, step-time variability and foot placement predictability, using steady-state data and subject-specific thresholds, detected impaired balance with the highest accuracy (≥86%), whereas other metrics were less effective (≤68%). Incorporating perturbation data did not improve accuracy of these metrics, although this comparison was limited by the small amount of perturbation data included and analyzed. Subject-specific baseline measurements improved the detection of changes in balance ability. Thus, in clinical practice, taking baseline measurements might improve the detection of impairment due to aging or disease progression.

ABSTRACT The exotendon is a passive device that reduces the energetic cost of running at 2.7 m/s, but its potential benefits at higher speeds remain unknown. Experimental testing is challenging because of the wide range of conditions that must be tested. Here, we use muscle-driven simulations to overcome this challenge and inform exotendon design. We validated a simulation framework that estimates changes in energy expenditure, body kinematics, and muscle activations when simulated subjects run with and without an exotendon. Simulations of people running at 4 m/s with the exotendon that saved energy at 2.7 m/s predicted a 10% reduction in energy cost compared to natural running. We then performed simulations of 25 designs and found that many of the designs saved energy. A longer, stiffer exotendon yielded slightly greater energy savings (12%). Longer and more compliant exotendons offered little savings. We plan to test a limited set of our simulation predictions in an experiment to evaluate their accuracy and assess how an exotendon impacts running performance at 4 m/s. The purpose of this paper is to present the simulation results and to make predictions about the performance of the runner-exotendon system in experiments. This paper has been posted before the experiments have begun to avoid informing the predictions from the experimental results.

Reconstructing 3D human movements from video sequences is an important task in the fields of computer vision, graphics, and biomechanics. Although much progress has been made to infer 3D human mesh based on visual contexts provided in video sequences, generalization to in-the-wild videos still remains challenging for existing human mesh recovery (HMR) methods. To overcome inaccurate prediction, they can perform a second step optimization that refines the inaccurate estimations continuously at test time. Most optimization methods seek fitting of the body joints in the image space with respect to pseudo ground truth predicted by an off-the-shelf key point detector. However, state-of-theart detectors still introduce errors, especially for challenging poses. In this work, we rethink the dependency on the 2D key point fitting paradigm and present NeuHMR, an optimization-based mesh recovery framework based on recent advances in neural rendering. Our method builds on Human Neural Radiance Fields that allow the refinement of human meshes through animatable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 D$</tex> renderings. We evaluated our method on two common benchmarks and validated its effectiveness.

Understanding the dynamics of human gait, including both motions and forces, is vital to promote human health and performance. Conventional gait analysis requires laboratory-based experiments and physics-based simulations to quantify gait dynamics and analyze how dynamics change with treatment, training, injury, and disease. However, the high costs associated with experiments and simulations has confined the use of gait dynamics to small-scale research studies. While deep learning models offer low-cost prediction, and can be highly expressive in fitting large-scale data, existing models have primarily been trained on small datasets with homogenous demographics and focused on predicting a single output. To overcome these limitations, we developed GaitDynamics, a generative foundation model for human gait that is trained on a large dataset with diverse participant demographics and gait patterns. GaitDynamics can be used for diverse tasks with different inputs, outputs, and clinical applications, which we illustrate in three examples: i) estimating ground reaction forces from kinematics with high accuracy and robustness even with missing kinematic data and for populations not included in the training dataset, ii) predicting the influence of gait modifications on knee loading without the need for resource-intensive experiments, and iii) predicting kinematic and force changes that occur with increasing running speeds. These representative tasks demonstrate that GaitDynamics makes accurate and rapid predictions in seconds based on flexible inputs, showing its potential to assess and optimize gait for injury prevention, disease treatment, and performance coaching. All data, code, and trained models are publicly shared.

Pose estimation has promised to impact healthcare by enabling more practical methods to quantify nuances of human movement and biomechanics. However, despite the inherent connection between pose estimation and biomechanics, these disciplines have largely remained disparate. For example, most current pose estimation benchmarks use metrics such as Mean Per Joint Position Error, Percentage of Correct Keypoints, or mean Average Precision to assess performance, without quantifying kinematic and physiological correctness - key aspects for biomechanics. To alleviate this challenge, we develop OpenCapBench to offer an easy-to-use unified benchmark to assess common tasks in human pose estimation, evaluated under physiological constraints. OpenCapBench computes consistent kinematic metrics through joints angles provided by an open-source musculoskeletal modeling software (OpenSim). Through OpenCapBench, we demonstrate that current pose estimation models use keypoints that are too sparse for accurate biomechanics analysis. To mitigate this challenge, we introduce SynthPose, a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data. Incorporating such fine-tuning on synthetic data of prior models leads to twofold reduced joint angle errors. Moreover, OpenCapBench allows users to benchmark their own developed models on our clinically relevant cohort. Overall, OpenCapBench bridges the computer vision and biomechanics communities, aiming to drive simultaneous advances in both areas.

BACKGROUND: Maintaining skeletal muscle health (i.e., muscle size and quality) is crucial for preserving mobility. Decreases in lower limb muscle volume and increased intramuscular fat (IMF) are common findings in people with impaired mobility. We developed an automated method to extract markers of leg muscle health, muscle volume and IMF, from MRI. We then explored their associations with age, body mass index (BMI), sex and voluntary force generation. METHODS: We trained (n = 34) and tested (n = 16) a convolutional neural network (CNN) to segment five muscle groups in both legs from fat-water MRI to explore muscle volume and IMF. In 95 participants (70 females, 25 males, mean age [standard deviation] = 34.2 (11.2) years, age range = 18-60 years), we explored associations between the CNN measures and age, BMI and sex, and then in a subset of 75 participants, we explored associations between CNN muscle volume, CNN IMF and maximum plantarflexion force after controlling for age, BMI and sex. RESULTS: ≥ 0.815 for all muscle groups) compared to manual segmentation. CNN muscle volume was positively associated with BMI across all muscle groups (p ≤ 0.001) but not with age (p ≥ 0.406). CNN IMF was positively associated with age for all muscle groups (p ≤ 0.015), and CNN IMF was positively associated with BMI for all muscle groups (p ≤ 0.043) except the right deep posterior compartment (p = 0.130). Males had greater CNN volume of all muscle groups (p < 0.001) except the left and right gastrocnemius (p ≥ 0.067). Gastrocnemius CNN IMF was greater in females (p ≤ 0.043). Plantarflexion force was positively associated with lateral compartment, soleus and gastrocnemius CNN volume (p ≤ 0.025) but not with CNN IMF (p ≥ 0.358). CONCLUSIONS: Computer-vision models combined with fat-water MRI permits the non-invasive, automatic assessment of leg muscle volume and IMF. Associations with age, BMI and sex are important when interpreting these measures. Markers of leg muscle health may enhance our understanding of the relationship between muscle health, force generation and mobility. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02157038.

Abstract For individuals with knee osteoarthritis, increased knee loading is linked to disease progression and pain. Some approaches to treating osteoarthritis, such as specialized footwear, braces, and powered exoskeletons, also increase the mass of the lower limbs, which could lead to increases in knee loads. Prior studies have investigated the effect of changes in torso mass and total body mass on peak knee contact forces, but the effects of increased leg mass remain unclear. In this study, we created musculoskeletal simulations informed by experimental data to estimate tibiofemoral knee contact force under different lower-limb segment mass conditions. The mass of the foot, shank, and thigh were varied by adding weights to each segment, separately and concurrently, as healthy young adults (N = 10) walked on a treadmill. Kinematics, kinetics, and muscle activity were recorded. Our simulations used an optimal control framework that enforced experimental kinematics while minimizing a combination of net joint moment errors and mismatch between measured and estimated muscle activity. The simulations revealed that adding mass to the lower-limb segments linearly increased early- and late-stance peaks in knee contact force, but that the slope of this relationship was different for each peak and each mass placement location. For each 1% of body weight (BW) added per limb (2% BW total) at the thigh, shank, and foot, early-stance peak knee contact force increased by 1.5%, 2.1%, and 5.9%, while late-stance peak contact force increased by 1.6%, 0.9% and 3.0%, respectively. Adding mass to the thigh and shank increases peak contact force at or below the rate of increase in body mass, while adding mass to the foot disproportionately increases peak knee contact force. These detrimental effects should be considered when designing interventions for osteoarthritis. Author summary For individuals with knee osteoarthritis, increased knee loading is linked to disease progression and pain. Some approaches to treating knee OA, such as specialized footwear, braces, and exoskeletons, increase the mass of the lower limbs, which could lead to increases in knee loads. However, the effects of changing lower limb segment mass on knee loading remain unclear. In this study, we created computer simulations informed by experimental data to estimate knee loads while walking with different amounts of added mass to the lower limb segments. We collected experimental data from 10 healthy participants while they walked on a treadmill with various amounts of mass added to their lower limb segments (thigh, shank, foot). We used these data and a biomechanically accurate model of each participant to construct realistic computer simulations, from which we obtained estimates of internal knee forces that contribute to total knee load. We found that peak knee load increased linearly with amount of added mass, but that this relationship differed by segment. Adding mass to the feet had the greatest effect on knee contact force. Our results can help to inform the design of interventions for knee osteoarthritis.

Rendering the visual appearance of moving humans from occluded monocular videos is a challenging task. Most existing research renders 3D humans under ideal conditions, requiring a clear and unobstructed scene. Those previous methods cannot be used to render humans in real-world scenes where obstacles may block the camera's view and lead to partial occlusions. In this work, we present Wild2Avatar, a neural rendering approach catered for occluded in-the-wild monocular videos. We propose occlusion-aware scene parameterization for decoupling the scene into three parts - occlusion, human, and background. Additionally, extensive objective functions are designed to help enforce the decoupling of the human from both the occlusion and the background and to ensure the completeness of the human model. Wild2Avatar is verified with experiments on 14 challenging in-the-wild videos.

Motivation: To unveil the mechanism of preventative action offered by eccentric exercise regimes towards hamstring strain injuries, understanding muscle adaptations at microstructural level is crucial. Goal(s): To investigate microstructural adaptations in hamstring muscles post 9-weeks of eccentric NHE using diffusion tensor imaging (DTI) metrics like axial (AD), mean (MD), and radial (RD) diffusivities. Approach: Ten participants underwent Dixon and DTI scans pre and post 9-weeks of supervised eccentric NHE training. Results: Post intervention, significant increases in AD, MD, and RD were observed, suggesting muscle hypertrophy, exercise-induced microtrauma, structural remodelling and potential Type II muscle fiber adaptations. Impact: This study explored the ability of DTI to provide novel insights into microstructural adaptations of hamstring muscle to eccentric training. The findings highlight hypertrophy, structural remodelling, and fiber type shifts, advancing injury prevention and rehabilitation strategies through a fiber-level perspective.

Motivation: Physical therapy and exercise are proposed to slow down the aging-associated loss in muscle strength, but it is currently not known which compositional or architectural aspects of skeletal muscle cause reduced muscle strength. Goal(s): Our goal was to exploit quantitative MRI to identify determinants of muscle strength production across the lifespan. Approach: We used quantitative MRI (fat fraction, diffusion parameters, fiber length and muscle T2) to predict quadriceps torque (n=24, 30-80 y/o). Results: We found that the inclusion of FA greatly improved the prediction of torque over morphology alone. This might be explained with modifications of fiber typing with aging. Impact: We have demonstrated that DTI parameters provide quantitative metrics of muscle quality which can be used to study force production in skeletal muscle, independently of volume. These compositional aspects might be amenable to interventions and provide specific targets for treatment.

Motivation: Osteoarthritis is a whole joint disease that requires quantification, localization, and visualization of disease related features of bones and cartilage. Goal(s): To develop a novel neural shape model (NSM) that can encode and reconstruct bone and cartilage shape, while quantifying localized features of OA. Approach: We trained a NSM on 6,325 knees and compared its reconstructions to a conventional statistical shape model and its ability to predict localized disease to a convolutional neural network. Results: The NSM reconstructed tissues with cartilage thickness correlations &amp;gt;0.993. NSM representations accurately diagnosed OA and predicted localized severity of osteophytes and cartilage defects better than a CNN. Impact: Our NSM can reconstruct whole bone and cartilage morphology, while encoding localized pathology specific information. Research use of the NSM can unlock novel insights into OA pathophysiology. Clinical deployment would enable automated insights into whole joint health.

OBJECTIVE: Hamstring injuries are common in field-based sports and reinjury rates are high. Recent evidence suggests that hamstring injuries often occur during accelerative running, but investigations of hamstring mechanics have primarily considered constant-speed running. Thus, our objective was to compare hamstring lengths and velocities between accelerative running and constant-speed running. METHODS: We recorded videos of 10 participants during six accelerative running trials and six constant-speed running trials. We used OpenCap to estimate body segment kinematics and a three-dimensional musculoskeletal model to compute peak length and step-average lengthening velocity of the biceps femoris (long head) muscle-tendon unit. We compared running conditions using linear mixed models with running speed as the independent variable. RESULTS: At running speeds below 75% of top speed, accelerative running resulted in greater peak lengths than constant-speed running. For example, the peak hamstring muscle-tendon length when a person accelerated from running at only 50% of top speed was equivalent to running at a constant 88% of top speed. Lengthening velocities were greater during accelerative running at all running speeds. Differences in hip flexion kinematics drove the greater peak lengths and lengthening velocities observed in accelerative running. CONCLUSIONS: Hamstrings are subjected to longer lengths and faster lengthening velocities in accelerative running than in constant-speed running. This provides a potential biomechanical perspective toward understanding the occurrence of hamstring injuries during acceleration. Our results suggest that coaches and sports medicine staff should consider the accelerative nature of running in addition to running speed to quantify exposure to high-risk circumstances with long lengths and fast lengthening velocities of the hamstrings.

BACKGROUND: Eccentric training, such as Nordic hamstring exercise (NHE) training, is commonly used as a preventive measure for hamstring strains. Eccentric training is believed to induce lengthening of muscle fascicles and to be associated with the addition of sarcomeres in series within muscle fibers. However, the difficulty in measuring sarcomere adaptation in human muscles has severely limited information about the precise mechanisms of adaptation. This study addressed this limitation by measuring the multiscale hamstring muscle adaptations in response to 9 weeks of NHE training and 3 weeks of detraining. METHODS: Twelve participants completed 9 weeks of supervised NHE training, followed by a 3-week detraining period. We assessed biceps femoris long-head (BFlh) muscle fascicle length, sarcomere length, and serial sarcomere number in the central and distal regions of the muscle. Additionally, we measured muscle volume and eccentric strength at baseline, post-training, and post-detraining. RESULTS: NHE training over 9 weeks induced significant architectural and strength adaptations in the BFlh muscle. Fascicle length increased by 19% in the central muscle region and 33% in the distal muscle region. NHE also induced increases in serial sarcomere number (25% in the central region and 49% in the distal region). BFlh muscle volume increased by 8%, and knee flexion strength increased by 40% with training. Following 3 weeks of detraining, fascicle length decreased by 12% in the central region and 16% in the distal region along with reductions in serial sarcomere number. CONCLUSION: Nine weeks of NHE training produced substantial, region-specific increases in BFlh muscle fascicle length, muscle volume, and force generation. The direct measurement of sarcomere lengths revealed that the increased fascicle length was accompanied by the addition of sarcomeres in series within the muscle fascicles.

This randomized clinical trial evaluated the effectiveness of short, digital interventions in improving physical activity and pain for individuals with knee osteoarthritis. We compared a digital mindset intervention, focusing on adaptive mindsets (e.g., osteoarthritis is manageable), to a digital education intervention and a no-intervention group. 408 participants with knee osteoarthritis completed the study online in the US. The mindset intervention significantly improved mindsets compared to both other groups (P < 0.001) and increased physical activity levels more than the no-intervention group (mean = 28.6 points, P = 0.001), but pain reduction was not significant. The mindset group also showed significantly greater improvements in the perceived need for surgery, self-imposed physical limitations, fear of movement, and self-efficacy than the no-intervention and education groups. This trial demonstrates the effectiveness of brief digital interventions in educating about osteoarthritis and further highlights the additional benefits of improving mindsets to transform patients' approach to disease management. The study was prospectively registered (ClinicalTrials.gov: NCT05698368, 2023-01-26).

Abstract Sprint performance is a priority for coaches and athletes. Several kinematic variables, including horizontal touchdown distance (HTD) and inter-knee touchdown distance (IKTD), are targeted by coaches to increase top sprinting speed. However, the results of past research are conflicting, potentially due to the use of experimental inter-athlete study designs where it is not possible to establish cause-effect relationships. In this study, we used a predictive simulation approach to assess cause-effect relationships between HTD and IKTD and sprinting speed. We scaled a three-dimensional musculoskeletal model to match the anthropometry of an international caliber male sprinter, and generated predictive simulations of a single symmetric step of top-speed sprinting using a direct collocation optimal control framework. We first used our simulation framework to establish the model’s top speed with minimal constraints on touchdown kinematics (the optimal simulation). Then, in additional simulations we enforced specific HTD or IKTD values (± 2, 4 and 6 cm compared to optimal). The model achieved a top speed of 11.85 m/s in the optimal simulation. Shortening HTD by 6 cm reduced speed by 7.3%, while lengthening HTD by 6 cm had a smaller impact on speed, with a 1.6% reduction. Speed in the simulation was insensitive to the IKTD changes we tested. The results of our simulations indicate there is an optimal HTD to maximize sprinting speed, providing support for coaches and athletes to adjust this technique variable. Conversely, our results do not provide evidence to support utilizing IKTD as a key technique variable for speed enhancement. We share the simulation framework so researchers can explore the effects of additional modifications on sprinting performance ( https://github.com/nicos1993/Pred_Sim_Sprinting ).