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Fu X, Withers J, Miyamae JA, Moore TY. ArborSim: Articulated, branching, OpenSim routing for constructing models of multi-jointed appendages with complex muscle-tendon architecture. PLoS Comput Biol 2024; 20:e1012243. [PMID: 38968305 PMCID: PMC11253963 DOI: 10.1371/journal.pcbi.1012243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 07/17/2024] [Accepted: 06/10/2024] [Indexed: 07/07/2024] Open
Abstract
Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints-often more than three-and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim, customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that among various parameters-the number of tendon branches, the number of joints between branches, and the ratio of muscle fiber length to muscle tendon unit length-the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method. Notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim, we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.
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Affiliation(s)
- Xun Fu
- Robotics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jack Withers
- Computer Science, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Juri A. Miyamae
- Robotics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Talia Y. Moore
- Robotics, University of Michigan, Ann Arbor, Michigan, United States of America
- Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan, Ann Arbor, Michigan, United States of America
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Xiang L, Gao Z, Wang A, Shim V, Fekete G, Gu Y, Fernandez J. Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors. Front Bioeng Biotechnol 2024; 12:1377383. [PMID: 38650752 PMCID: PMC11033368 DOI: 10.3389/fbioe.2024.1377383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.
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Affiliation(s)
- Liangliang Xiang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Zixiang Gao
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
- Faculty of Engineering, University of Pannonia, Veszprém, Hungary
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Center for Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Gusztáv Fekete
- Vehicle Industry Research Center, Széchenyi István University, Győr, Hungary
| | - Yaodong Gu
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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Xiang L, Gu Y, Gao Z, Yu P, Shim V, Wang A, Fernandez J. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Comput Biol Med 2024; 170:108016. [PMID: 38277923 DOI: 10.1016/j.compbiomed.2024.108016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024]
Abstract
The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.
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Affiliation(s)
- Liangliang Xiang
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | - Zixiang Gao
- Faculty of Sports Science, Ningbo University, Ningbo, China; Faculty of Engineering, University of Pannonia, Veszprém, Hungary
| | - Peimin Yu
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Center for Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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Kainz H, Koller W, Wallnöfer E, Bader TR, Mindler GT, Kranzl A. A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining. Sci Rep 2024; 14:3567. [PMID: 38347085 PMCID: PMC10861532 DOI: 10.1038/s41598-024-53857-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°-29°, tibial torsion 19°-38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°-52°, tibial torsion 3°-50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.
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Affiliation(s)
- Hans Kainz
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria.
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria.
| | - Willi Koller
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria
| | - Elias Wallnöfer
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6a (USZ II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria
| | - Till R Bader
- Department of Radiology, Orthopaedic Hospital Speising, Vienna, Austria
| | - Gabriel T Mindler
- Department of Paediatric Orthopaedics and Foot Surgery, Orthopaedic Hospital Speising, Vienna, Austria
- Vienna Bone and Growth Center, Vienna, Austria
| | - Andreas Kranzl
- Vienna Bone and Growth Center, Vienna, Austria
- Laboratory for Gait and Movement Analysis, Orthopaedic Hospital Speising, Vienna, Austria
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Hwang GM, Kulwatno J, Cruz TH, Chen D, Ajisafe T, Monaco JD, Nitkin R, George SM, Lucas C, Zehnder SM, Zhang LT. NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies. J Neuroeng Rehabil 2024; 21:17. [PMID: 38310271 PMCID: PMC10837948 DOI: 10.1186/s12984-024-01308-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .
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Affiliation(s)
- Grace M Hwang
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA.
| | - Jonathan Kulwatno
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Theresa H Cruz
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Daofen Chen
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA
| | - Toyin Ajisafe
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Joseph D Monaco
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA
| | - Ralph Nitkin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Stephanie M George
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Carol Lucas
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Steven M Zehnder
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Lucy T Zhang
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
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Moura FA, Pelegrinelli ARM, Catelli DS, Kowalski E, Lamontagne M, da Silva Torres R. On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods. Sci Rep 2024; 14:1379. [PMID: 38228640 PMCID: PMC10791669 DOI: 10.1038/s41598-023-50481-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/20/2023] [Indexed: 01/18/2024] Open
Abstract
Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.
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Affiliation(s)
- Felipe Arruda Moura
- Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil.
- Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands.
| | - Alexandre R M Pelegrinelli
- Laboratory of Applied Biomechanics, Sport Sciences Department, State University of Londrina, Londrina, Brazil
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Danilo S Catelli
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
- Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Erik Kowalski
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Mario Lamontagne
- Human Movement Biomechanics Laboratory, University of Ottawa, Ottawa, Canada
| | - Ricardo da Silva Torres
- Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands.
- Department of ICT and Natural Sciences, NTNU-Norwegian University of Science and Technology, Ålesund, Norway.
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Gill HS. CORR Insights®: Are Abnormal Muscle Biomechanics and Patient-reported Outcomes Associated in Patients With Hip Dysplasia? Clin Orthop Relat Res 2023; 481:2390-2391. [PMID: 37498284 PMCID: PMC10642880 DOI: 10.1097/corr.0000000000002787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 07/28/2023]
Affiliation(s)
- Harinderjit S Gill
- Professor of Mechanical Engineering, University of Bath - Claverton Down, Bath, UK
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