Hamstrings are stretched more and faster during accelerative running compared to speed-matched constant speed running.

Background: Hamstring strain injuries are associated with significant time away from sport and high reinjury rates. Recent evidence suggests that hamstring injuries often occur during accelerative running, but investigations of hamstring mechanics have primarily examined constant speed running on a treadmill. To help fill this gap in knowledge, this study compares hamstring lengths and lengthening velocities between accelerative running and constant speed overground running. Methods: We recorded 2 synchronized videos of 10 participants (5 female, 5 male) during 6 accelerative running trials and 6 constant speed running trials. We used OpenCap (a markerless motion capture system) to estimate body segment kinematics for each trial and a 3-dimensional musculoskeletal model to compute peak length and step-average lengthening velocity of the biceps femoris (long head) muscle-tendon unit. To compare running conditions, we used linear mixed regression models with running speed (normalized by the subject-specific maximum) 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 primarily drove the greater peak muscle-tendon lengths and lengthening velocities observed in accelerative running. Conclusion: Hamstrings are subjected to longer muscle-tendon lengths and faster lengthening velocities in accelerative running compared to constant speed running. This provides a biomechanical explanation for the observation that hamstring strain injuries often occur during acceleration. Our results suggest coaches who monitor exposure to high-risk circumstances (long lengths, fast lengthening velocities) should consider the accelerative nature of running in addition to running speed.

Lower-Limb Exoskeletons Appeal to Both Clinicians and Older Adults, Especially for Fall Prevention and Joint Pain Reduction.

Exoskeletons are a burgeoning technology with many possible applications to improve human life; focusing the effort of exoskeleton research and development on the most important features is essential for facilitating adoption and maximizing positive societal impact. To identify important focus areas for exoskeleton research and development, we conducted a survey with 154 potential users (older adults) and another survey with 152 clinicians. The surveys were conducted online and to ensure a consistent concept of an exoskeleton across respondents, an image of a hip exoskeleton was shown during exoskeleton-related prompts. The survey responses indicate that both older adults and clinicians are open to using exoskeletons, fall prevention and joint pain reduction are especially important features, and users are likely to wear an exoskeleton in the scenarios when it has the greatest opportunity to help prevent a fall. These findings can help inform future exoskeleton research and guide the development of devices that are accepted, used, and provide meaningful benefit to users.

Marker Data Enhancement For Markerless Motion Capture.

Objective: Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, addresses this issue using a deep learning model-the marker enhancer-that transforms sparse keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer. Methods: We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements. Results: The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1°, max: 8.7°) compared to using video keypoints (mean: 9.6°, max: 43.1°) and OpenCap's original enhancer (mean: 5.3°, max: 11.5°). It also better generalized to unseen, diverse movements (mean: 4.1°, max: 6.7°) than OpenCap's original enhancer (mean: 40.4°, max: 252.0°). Conclusion: Our marker enhancer demonstrates both accuracy and generalizability across diverse movements. Significance: We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.