Biology subjects

Falisse, A.

Publications and source records attributed to Falisse, A..

9 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↗

bioRxiv2024

Marker Data Enhancement For Markerless Motion Capture

ObjectiveHuman 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. MethodsWe 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. ResultsThe marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1{degrees}, max: 8.7{degrees}) compared to using video keypoints (mean: 9.6{degrees}, max: 43.1{degrees}) and OpenCaps original enhancer (mean: 5.3{degrees}, max: 11.5{degrees}). It also better generalized to unseen, diverse movements (mean: 4.1{degrees}, max: 6.7{degrees}) than OpenCaps original enhancer (mean: 40.4{degrees}, max: 252.0{degrees}). ConclusionOur marker enhancer demonstrates both accuracy and generalizability across diverse movements. SignificanceWe integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

bioengineering↗

bioRxiv2024

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

BackgroundHamstring 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. MethodsWe 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. ResultsAt 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. ConclusionHamstrings 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.

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

Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking

Physics-based predictive simulations have been shown to capture many salient features of human walking. Yet they often fail to produce realistic stance knee mechanics and terminal stance ankle plantarflexion. While the influence of the performance criterion on the predicted walking pattern has been previously studied, the influence of the mechanics has been less explored. Here, we investigated the influence of two mechanical assumptions on the predicted walking pattern: the complexity of the foot segment and the stiffness of the Achilles tendon. We found, through three-dimensional muscle-driven predictive simulations of walking, that modeling the toes and metatarsophalangeal joints, and thus using two-segment instead of single-segment foot models, contributed to robustly eliciting physiological stance knee flexion angles, knee extension torques, and knee extensor activity. Yet modeling toe joints did not improve ankle kinematics, nor did decreasing the Achilles tendon stiffness. The lack of predicted terminal stance ankle plantarflexion thereby remains an open question. Overall, this simulation study shows that not only the performance criterion but also mechanical assumptions affect predictive simulations of walking. Improving the realism of predictive simulations is required for their application in clinical contexts. Here, we suggest that using complex models is needed to yield such realism.

bioengineering↗

bioRxiv2019

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations of movement can provide insights needed to help humans regain mobility after injuries and design robots that interact with humans. Here, we introduce Open-Sim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation but requires extensive technical expertise to implement. Moco frees researchers from implementing direct collocation themselves, allowing them to focus on their scientific questions. The software can handle the wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which are common in musculoskeletal models. To show Mocos abilities, we first solve for muscle activity that produces an observed walking motion while minimizing muscle excitations and knee joint loading. Then, we predict a squat-to-stand motion and optimize the stiffness of a passive assistive knee device. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand human and animal movement.

bioengineering↗

bioRxiv2019

Physics-based predictive simulations to explore the differential effects of motor control and musculoskeletal deficits on gait dysfunction in cerebral palsy: a retrospective case study

Model-based simulations of walking have the theoretical potential to support clinical decision making by predicting the functional outcome of treatments in terms of walking performance. Yet before using such simulations in clinical practice, their ability to identify the main treatment targets in specific patients needs to be demonstrated. In this study, we generated predictive simulations of walking with a medical imaging based neuro-musculoskeletal model of a child with cerebral palsy presenting crouch gait. We explored the influence of altered muscle-tendon properties, reduced neuromuscular control complexity, and spasticity on gait function in terms of joint kinematics, kinetics, muscle activity, and metabolic cost of transport. We modeled altered muscle-tendon properties by personalizing Hill-type muscle-tendon parameters based on data collected during functional movements, simpler neuromuscular control by reducing the number of independent muscle synergies, and spasticity through delayed muscle activity feedback from muscle force and force rate. Our simulations revealed that, in the presence of aberrant musculoskeletal geometries, altered muscle-tendon properties rather than reduced neuromuscular control complexity and spasticity were the primary cause of the crouch gait pattern observed for this child, which is in agreement with the clinical examination. These results suggest that muscle-tendon properties should be the primary target of interventions aiming to restore a more upright gait pattern for this child. This suggestion is in line with the gait analysis following muscle-tendon property and bone deformity corrections. The ability of our simulations to distinguish the contribution of different impairments on walking performance opens the door for identifying targeted treatment strategies with the aim of designing optimized interventions for neuro-musculoskeletal disorders.

bioengineering↗

bioRxiv2019

Algorithmic differentiation improves the computational efficiency of OpenSim-based optimal control simulations of movement

Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primarily aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based optimal control simulations. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to perform optimal control simulations. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 {+/-} 0.1 and 17.8 {+/-} 4.9 times faster than using FD, and between 3.6 {+/-} 0.3 and 12.3 {+/-} 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of optimal control simulations as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of optimal control simulations, which will facilitate their use for biomechanical applications.

bioengineering↗