Ten Steps to Becoming a Musculoskeletal Simulation Expert: A Half-Century of Progress and Outlook for the Future

[1]  Elizabeth L. Murnane,et al.  Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. , 2022, Annual review of public health.

[2]  J. M. Font-Llagunes,et al.  Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review , 2022, Multibody System Dynamics.

[3]  C. K. Liu,et al.  Rapid bilevel optimization to concurrently solve musculoskeletal scaling, marker registration, and inverse kinematic problems for human motion reconstruction , 2022, bioRxiv.

[4]  S. Delp,et al.  OpenCap: 3D human movement dynamics from smartphone videos , 2022, bioRxiv.

[5]  Rachel W. Jackson,et al.  Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force , 2022, Scientific Reports.

[6]  Alejandro F Frangi,et al.  Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG , 2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Miguel Tavares da Silva,et al.  On the Modeling of Biomechanical Systems for Human Movement Analysis: A Narrative Review , 2022, Archives of Computational Methods in Engineering.

[8]  C. K. Liu,et al.  Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation , 2022, SIGGRAPH Asia.

[9]  S. Delp,et al.  Assessing inertial measurement unit locations for freezing of gait detection and patient preference , 2021, medRxiv.

[10]  Chadd W. Clary,et al.  The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions , 2021, Sensors.

[11]  D. Haelewaters,et al.  Ten simple rules for Global North researchers to stop perpetuating helicopter research in the Global South , 2021, PLoS Comput. Biol..

[12]  Carmichael F. Ong,et al.  OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations , 2021, bioRxiv.

[13]  Nicholas A. Bianco,et al.  Coupled exoskeleton assistance simplifies control and maintains metabolic benefits: A simulation study , 2021, bioRxiv.

[14]  P. J. Bishop,et al.  Computational modelling of muscle fibre operating ranges in the hindlimb of a small ground bird (Eudromia elegans), with implications for modelling locomotion in extinct species , 2021, PLoS Comput. Biol..

[15]  Kris Kitani,et al.  SimPoE: Simulated Character Control for 3D Human Pose Estimation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  S. Delp,et al.  An open-source and wearable system for measuring 3D human motion in real-time , 2021, bioRxiv.

[17]  F. De Groote,et al.  Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait , 2021, Proceedings of the Royal Society B.

[18]  Konstantinos Moustakas,et al.  Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation , 2021, Sensors.

[19]  Floriant Labarrière,et al.  Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review , 2020, Sensors.

[20]  Peter J. Bishop,et al.  How to build a dinosaur: Musculoskeletal modeling and simulation of locomotor biomechanics in extinct animals , 2020, Paleobiology.

[21]  I. Jonkers,et al.  In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice? , 2020, Applied Sciences.

[22]  Nicholas B Bolus,et al.  Estimating Knee Joint Load Using Acoustic Emissions During Ambulation , 2020, Annals of Biomedical Engineering.

[23]  Clare Minahan,et al.  Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers , 2020, Sensors.

[24]  Jylana L. Sheats,et al.  Effects of Counseling by Peer Human Advisors vs Computers to Increase Walking in Underserved Populations: The COMPASS Randomized Clinical Trial. , 2020, JAMA internal medicine.

[25]  Christian Theobalt,et al.  PhysCap , 2020, ACM Trans. Graph..

[26]  Sergey Levine,et al.  Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation , 2020, Journal of NeuroEngineering and Rehabilitation.

[27]  Roger Quinn,et al.  A three-dimensional musculoskeletal model of the dog , 2020, Scientific Reports.

[28]  S. Delp,et al.  Pre-operative gastrocnemius lengths in gait predict outcomes following gastrocnemius lengthening surgery in children with cerebral palsy , 2020, PloS one.

[29]  Katherine M. Steele,et al.  Design and Development of a Quasi-Passive Transtibial Biarticular Prosthesis to Replicate Gastrocnemius Function in Walking. , 2020, Journal of medical devices.

[30]  Cem Ersoy,et al.  Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters , 2019, IEEE Journal of Biomedical and Health Informatics.

[31]  David G. Lloyd,et al.  Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury , 2019, Front. Neurorobot..

[32]  C. Mazzà,et al.  Muscle recruitment strategies can reduce joint loading during level walking. , 2019, Journal of biomechanics.

[33]  Scott L. Delp,et al.  OpenSim Moco: Musculoskeletal optimal control , 2019, bioRxiv.

[34]  John McPhee,et al.  A review of simulation methods for human movement dynamics with emphasis on gait , 2019, Multibody System Dynamics.

[35]  Antonie J van den Bogert,et al.  Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. , 2019, Journal of biomechanics.

[36]  Christopher L. Dembia,et al.  Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies , 2019, Journal of the Royal Society Interface.

[37]  Barbara McGillivray,et al.  The citation advantage of linking publications to research data , 2019, PloS one.

[38]  Thomas Geijtenbeek,et al.  SCONE: Open Source Software for Predictive Simulation of Biological Motion , 2019, J. Open Source Softw..

[39]  Daniel C McFarland,et al.  Spatial Dependency of Glenohumeral Joint Stability during Dynamic Unimanual and Bimanual Pushing and Pulling. , 2019, Journal of biomechanical engineering.

[40]  Nikolaus F. Troje,et al.  AMASS: Archive of Motion Capture As Surface Shapes , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[41]  Scott L. Delp,et al.  Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations , 2019, bioRxiv.

[42]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[43]  Madalina Fiterau,et al.  Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. , 2018, Journal of biomechanics.

[44]  Dario Farina,et al.  Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling , 2018, Journal of neural engineering.

[45]  Ilse Jonkers,et al.  SimCP: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy , 2018, Front. Neurorobot..

[46]  Christie K. Ferreira,et al.  Recovery of Over‐Ground Walking after Chronic Motor Complete Spinal Cord Injury , 2018, The New England journal of medicine.

[47]  Ayman Habib,et al.  OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement , 2018, PLoS Comput. Biol..

[48]  Andrew S Merryweather,et al.  The inclusion of hyoid muscles improve moment generating capacity and dynamic simulations in musculoskeletal models of the head and neck , 2018, PloS one.

[49]  I. Jonkers,et al.  Ranking of osteogenic potential of physical exercises in postmenopausal women based on femoral neck strains , 2018, PloS one.

[50]  Sergey Levine,et al.  Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning , 2018, ArXiv.

[51]  Colin R. Smith,et al.  Knee Joint Loading in Healthy Adults During Functional Exercises: Implications for Rehabilitation Guidelines , 2018, The Journal of orthopaedic and sports physical therapy.

[52]  Christopher Ré,et al.  Snorkel: Rapid Training Data Creation with Weak Supervision , 2017, Proc. VLDB Endow..

[53]  James M. Wakeling,et al.  Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks , 2017, Annals of Biomedical Engineering.

[54]  Michael C. Rosenberg,et al.  Simulated impacts of ankle foot orthoses on muscle demand and recruitment in typically-developing children and children with cerebral palsy and crouch gait , 2017, PloS one.

[55]  Scott L Delp,et al.  Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads , 2017, PloS one.

[56]  J. Leskovec,et al.  Large-scale physical activity data reveal worldwide activity inequality , 2017, Nature.

[57]  Monica Reggiani,et al.  Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[58]  B. Hargreaves,et al.  Cluster analysis of quantitative MRI T2 and T1ρ relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T. , 2017, Osteoarthritis and cartilage.

[59]  Matthew S. DeMers,et al.  Preparatory co-activation of the ankle muscles may prevent ankle inversion injuries. , 2017, Journal of biomechanics.

[60]  Mark de Zee,et al.  Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture , 2016, Sensors.

[61]  Soha Pouya,et al.  Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running , 2016, PloS one.

[62]  Scott L. Delp,et al.  Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait , 2016, IEEE Transactions on Biomedical Engineering.

[63]  Jonas Rubenson,et al.  Inferring muscle functional roles of the ostrich pelvic limb during walking and running using computer optimization , 2016, Journal of The Royal Society Interface.

[64]  Massimo Sartori,et al.  CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. , 2015, Journal of biomechanics.

[65]  Jonas Rubenson,et al.  Musculoskeletal modelling of an ostrich (Struthio camelus) pelvic limb: influence of limb orientation on muscular capacity during locomotion , 2015, PeerJ.

[66]  Ajay Seth,et al.  Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. , 2015, Journal of biomechanical engineering.

[67]  Anca Velisar,et al.  Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model , 2015, Computer methods in biomechanics and biomedical engineering.

[68]  Matthew S. DeMers,et al.  Changes in tibiofemoral forces due to variations in muscle activity during walking , 2014, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[69]  Scott L Delp,et al.  Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. , 2014, Gait & posture.

[70]  B. R. Umberger,et al.  A three-dimensional musculoskeletal model of the chimpanzee (Pan troglodytes) pelvis and hind limb , 2013, Journal of Experimental Biology.

[71]  Antonie J. van den Bogert,et al.  A real-time system for biomechanical analysis of human movement and muscle function , 2013, Medical & Biological Engineering & Computing.

[72]  Jill S Higginson,et al.  Understanding compensatory strategies for muscle weakness during gait by simulating activation deficits seen post-stroke. , 2013, Gait & posture.

[73]  Benjamin J Fregly,et al.  Update on grand challenge competition to predict in vivo knee loads. , 2013, Journal of biomechanical engineering.

[74]  Katherine M Steele,et al.  How much muscle strength is required to walk in a crouch gait? , 2012, Journal of biomechanics.

[75]  Jeffrey A Reinbolt,et al.  A platform for dynamic simulation and control of movement based on OpenSim and MATLAB. , 2012, Journal of biomechanics.

[76]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[77]  Marcus G Pandy,et al.  Grand challenge competition to predict in vivo knee loads , 2012, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[78]  Matthew S. DeMers,et al.  Compressive tibiofemoral force during crouch gait. , 2012, Gait & posture.

[79]  Dominic James Farris,et al.  The mechanics and energetics of human walking and running: a joint level perspective , 2012, Journal of The Royal Society Interface.

[80]  Scott L Delp,et al.  Can biomechanical variables predict improvement in crouch gait? , 2011, Gait & posture.

[81]  B. R. Umberger,et al.  Stance and swing phase costs in human walking , 2010, Journal of The Royal Society Interface.

[82]  D. Bose,et al.  Influence of pre-collision occupant parameters on injury outcome in a frontal collision. , 2010, Accident; analysis and prevention.

[83]  Marko Ackermann,et al.  Optimality principles for model-based prediction of human gait. , 2010, Journal of biomechanics.

[84]  J Maxwell Donelan,et al.  Dynamic Principles of Gait and Their Clinical Implications , 2010, Physical Therapy.

[85]  I Jonkers,et al.  A physiology based inverse dynamic analysis of human gait: potential and perspectives , 2009, Computer methods in biomechanics and biomedical engineering.

[86]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[87]  Jonathan P. Walter,et al.  Decreased knee adduction moment does not guarantee decreased medial contact force during gait , 2009, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[88]  E. Alentorn-Geli,et al.  Prevention of non-contact anterior cruciate ligament injuries in soccer players. Part 1: Mechanisms of injury and underlying risk factors , 2009, Knee Surgery, Sports Traumatology, Arthroscopy.

[89]  May Q. Liu,et al.  Muscle contributions to support and progression over a range of walking speeds. , 2008, Journal of biomechanics.

[90]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[91]  Jeffrey A. Reinbolt,et al.  From the Selectedworks of Jeffrey A. Reinbolt Design of Patient-specific Gait Modifications for Knee Osteoarthritis Rehabilitation Design of Patient-specific Gait Modifications for Knee Osteoarthritis Rehabilitation , 2022 .

[92]  Michael Damsgaard,et al.  Analysis of musculoskeletal systems in the AnyBody Modeling System , 2006, Simul. Model. Pract. Theory.

[93]  Stephen J Piazza,et al.  Muscle-driven forward dynamic simulations for the study of normal and pathological gait , 2006, Journal of NeuroEngineering and Rehabilitation.

[94]  J. Hutchinson,et al.  Analysis of hindlimb muscle moment arms in Tyrannosaurus rex using a three-dimensional musculoskeletal computer model: implications for stance, gait, and speed , 2005, Paleobiology.

[95]  F. Zajac,et al.  Contributions of the individual ankle plantar flexors to support, forward progression and swing initiation during walking. , 2001, Journal of biomechanics.

[96]  M. Pandy,et al.  Dynamic optimization of human walking. , 2001, Journal of biomechanical engineering.

[97]  J. Heegaard,et al.  Predictive algorithms for neuromuscular control of human locomotion. , 2001, Journal of biomechanics.

[98]  F. Zajac,et al.  Force- and moment-generating capacity of lower-extremity muscles before and after tendon lengthening. , 1992, Clinical orthopaedics and related research.

[99]  F.E. Zajac,et al.  Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: a computer simulation study , 1990, IEEE Transactions on Biomedical Engineering.

[100]  F.E. Zajac,et al.  An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures , 1990, IEEE Transactions on Biomedical Engineering.

[101]  C. R. Hargraves,et al.  DIRECT TRAJECTORY OPTIMIZATION USING NONLINEAR PROGRAMMING AND COLLOCATION , 1986 .

[102]  V. Edgerton,et al.  Muscle architecture of the human lower limb. , 1983, Clinical orthopaedics and related research.

[103]  F M van Krieken,et al.  A model of lower extremity muscular anatomy. , 1982, Journal of biomechanical engineering.

[104]  A Seireg,et al.  A mathematical model for evaluation of forces in lower extremeties of the musculo-skeletal system. , 1973, Journal of biomechanics.

[105]  D. Jacobson,et al.  Studies of human locomotion via optimal programming , 1971 .

[106]  D B Chaffin,et al.  A computerized biomechanical model-development of and use in studying gross body actions. , 1969, Journal of biomechanics.

[107]  Benjamin J. Ellis,et al.  FEBio: finite elements for biomechanics. , 2012, Journal of biomechanical engineering.

[108]  Will L. Johnson,et al.  A three-dimensional model of the rat hindlimb: musculoskeletal geometry and muscle moment arms. , 2008, Journal of biomechanics.

[109]  Thomas P Andriacchi,et al.  The influence of deceleration forces on ACL strain during single-leg landing: a simulation study. , 2007, Journal of biomechanics.

[110]  D. Theisen,et al.  Wheelchair Propulsion Biomechanics , 2001, Sports medicine.

[111]  M. Pandy,et al.  A Dynamic Optimization Solution for Vertical Jumping in Three Dimensions. , 1999, Computer methods in biomechanics and biomedical engineering.

[112]  F. Zajac,et al.  A musculoskeletal model of the human lower extremity: the effect of muscle, tendon, and moment arm on the moment-angle relationship of musculotendon actuators at the hip, knee, and ankle. , 1990, Journal of biomechanics.

[113]  F. Zajac,et al.  A planar model of the knee joint to characterize the knee extensor mechanism. , 1989, Journal of biomechanics.

[114]  M L Audu,et al.  A dynamic optimization technique for predicting muscle forces in the swing phase of gait. , 1987, Journal of biomechanics.

[115]  H. Hatze.  The complete optimization of a human motion , 1976 .