Biology subjects

Uhlrich, S. D.

Publications and source records attributed to Uhlrich, S. D..

6 recordsLinked to original sources

bioRxiv2025

Integrating Machine Learning with Musculoskeletal Simulation Improves OpenCap Video-Based Dynamics Estimation

ObjectiveMusculoskeletal dynamics influence the progression and rehabilitation of many movement-related conditions. However, accurately estimating whole-body dynamics using accessible tools, like smartphone video, remains challenging. Physics-based and machine learning (ML)-based dynamic predictions each offer advantages, but both approaches struggle to achieve both high accuracy and physical realism. Here, we created a hybrid ML-simulation framework to improve estimates of ground reaction forces, joint moments, and joint contact forces from smartphone video kinematics. MethodsWe used machine learning models to predict ground reaction forces and centers of pressure from video-based kinematics. The hybrid framework generates a dynamic simulation that tracks predicted forces and kinematics while enforcing dynamic consistency. We compared the hybrid models performance with a simulation-only approach and with ML forces applied through inverse dynamics. We evaluated mean absolute error from lab-based reference data (inverse dynamics from marker and force plate data) from 10 individuals walking. ResultsThe hybrid model had 29% lower joint moment errors compared to simulations (p<0.001) and 45% lower errors compared to the ML-only approach (p<0.001). It also reduced vertical ground force error by 40% compared to simulations. The hybrid approach improved key metrics of joint loading related to knee osteoarthritis progression by 13-30% compared to simulations. ConclusionOur hybrid model outperforms purely physics-based and ML approaches for estimating dynamics from smartphone video during walking. SignificanceThese methods move us closer to fast, accurate, and scalable assessments of whole-body musculoskeletal dynamics, which will enable large out-of-lab biomechanics studies and precision treatment of gait-related conditions.

physiology↗

bioRxiv2024

Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases

BackgroundAssessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread 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 is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable quantification of 3D 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. MethodsTo compare video-based analysis against 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-time sit-to-stand) while capturing additional disease-specific signatures of movement. ResultsVideo-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. ConclusionVideo-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. DescriptionThis study demonstrates that smartphone video-based biomechanical analysis can accurately replicate traditional timed function tests (TFTs) -- commonly used to diagnose and assess movement-related conditions like neuromuscular disorders -- while also capturing disease-specific movement patterns that TFTs fail to detect. By enabling sensitive and interpretable assessments in clinical settings, this approach offers a scalable and objective tool to enhance diagnosis, monitoring, and clinical trial design for neuromuscular disorders.

bioengineering↗

bioRxiv2023

How Connecting the Legs with a Spring Improves Human Running Economy

Connecting the legs with a spring attached to the shoelaces reduces the energy cost of running, but how the spring reduces the energy burden of individual muscles remains unknown. We generated muscle-driven simulations of seven individuals running with and without the spring to discern whether savings occurred during the stance phase or the swing phase, and to identify which muscles contributed to energy savings. We computed differences in muscle-level energy consumption, muscle activations, and changes in muscle-fiber velocity and force between running with and without the spring. Across participants, running with the spring reduced the measured rate of energy expenditure by 0.9 W/kg (8.3%). Simulations predicted a 1.4 W/kg (12.0%) reduction in the average rate of energy expenditure and correctly identified that the spring reduced rates of energy expenditure for all participants. Simulations showed most of the savings occurred during stance (1.5 W/kg), though the rate of energy expenditure was also reduced during swing (0.3 W/kg). The energetic savings were distributed across the quadriceps, hip flexor, hip abductor, hamstring, hip adductor, and hip extensor muscle groups, whereas no changes in the rate of energy expenditure were observed in the plantarflexor or dorsiflexor muscles. Energetic savings were facilitated by reductions in the rate of mechanical work performed by muscles and their estimated rate of heat production. The simulations provide insight into muscle-level changes that occur when utilizing an assistive device and the mechanisms by which a spring connecting the legs improves running economy.

bioengineering↗

bioRxiv2022

Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and a custom static optimization implementation was used to estimate MCF. Simulations were evaluated using experimental data from three subjects for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of 0.10 bodyweights and greater. These results suggest that a simple static optimization approach accurately detects directional effects on early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.

bioengineering↗

bioRxiv2022

OpenCap: 3D human movement dynamics from smartphone videos

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing movement dynamics using videos captured from smartphones. OpenCaps web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCaps practical utility through a 100-subject field study, where a clinician using OpenCap estimated movement dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

bioengineering↗

bioRxiv2021

Muscle coordination retraining inspired by musculoskeletal simulations: a study on reducing knee loading

Humans typically coordinate their muscles to meet movement objectives like minimizing energy expenditure. In the presence of pathology, new objectives gain importance, like reducing loading in an osteoarthritic joint, but people often do not change their muscle coordination patterns to meet these new objectives. Here we use musculoskeletal simulations to identify simple changes in coordination that can be taught by providing feedback of electromyographic activity to achieve a therapeutic goal--reducing joint loading. Our simulations predicted that changing the relative activation of the redundant ankle plantarflexors could reduce knee contact force during walking, but it was unclear whether humans could re-coordinate redundant muscles during a complex task like walking. With simple biofeedback of electromyographic activity, healthy individuals reduced the ratio of gastrocnemius to soleus muscle activation by 25{+/-}15% (p=0.004). The resulting "gastrocnemius avoidance" gait pattern reduced the late-stance peak of simulation-estimated knee contact force by 12{+/-}12% (p=0.029). Simulation-informed muscle coordination retraining could be a promising treatment for knee osteoarthritis and a powerful tool for optimizing coordination for a variety of rehabilitation and performance applications.

bioengineering↗