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Cited by 65Human Motion and AnimationMuscle activation and electromyography studiesMechanics and Biomechanics Studies

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

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Werling, Keenon, Bianco, Nicholas A., Raitor, Michael, Stingel, Jon, Hicks, Jennifer L., Collins, Steven H., Delp, Scott L., Liu, C. Karen·2023·PLOS ONE
DOI 10.1371/journal.pone.0295152PMC PMC10688959

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Abstract

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject’s skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org , which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

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  • Most studies on motion analysis focus on qualitative aspects, while standardized and accessible libraries that enable precise movement quantification in measurement units remain a challenge.

    “Although research on motion analysis is expanding, most studies focus on qualitative aspects, while standardized and accessible libraries that enable precise movement quantification in measurement units remain a challenge [3-5].”

    From PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos (2025) · cites this paper as [4]

    The Introduction (page unavailable) says quantitative movement analysis is resource-intensive and that a lack of automated tools to process and harmonize data restricts access. It does not say that most motion-analysis studies are qualitative or identify a lack of libraries for quantification in measurement units; it describes quantitative analysis as widely used. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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  1. [1]Fregly BJ, Besier TF, Lloyd DG, Delp SL, Banks SA, Pandy MG, et al. Grand challenge competition to predict in vivo knee loads. Journal of Orthopaedic Research. 2012; 30(4): 503– 513. doi: 10.1002/jor.22023 22161745 PMC4067494 · DOI
  2. [2]Lloyd DG, Besier TF. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics. 2003; 36(6): 765– 776. doi: 10.1016/S0021-9290(03)00010-1 12742444 · DOI
  3. [3]Besier TF, Lloyd DG, Ackland TR, Cochrane JL. Anticipatory effects on knee joint loading during running and cutting maneuvers. Medicine and Science in Sports and Exercise. 2001; 33(7): 1176– 1181. doi: 10.1097/00005768-200107000-00015 11445765 · DOI
  4. [6]Zhao D, Banks SA, Mitchell KH, D’Lima DD, Colwell CW Jr, Fregly BJ. Correlation between the knee adduction torque and medial contact force for a variety of gait patterns. Journal of Orthopaedic Research. 2007; 25(6): 789– 797. doi: 10.1002/jor.20379 17343285 · DOI
  5. [7]Walter JP, D’Lima DD, Colwell CW Jr, Fregly BJ. Decreased knee adduction moment does not guarantee decreased medial contact force during gait. Journal of Orthopaedic Research. 2010; 28(10): 1348– 1354. doi: 10.1002/jor.21142 20839320 PMC2984615 · DOI
  6. [8]Shull PB, Shultz R, Silder A, Dragoo JL, Besier TF, Cutkosky MR, et al. Toe-in gait reduces the first peak knee adduction moment in patients with medial compartment knee osteoarthritis. Journal of Biomechanics. 2013; 46(1): 122– 128. doi: 10.1016/j.jbiomech.2012.10.019 23146322 · DOI
  7. [9]Lerner ZF, DeMers MS, Delp SL, Browning RC. How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces. Journal of Biomechanics. 2015; 48(4): 644– 650. doi: 10.1016/j.jbiomech.2014.12.049 25595425 PMC4330122 · DOI
  8. [10]Sartori M, Fernandez J, Modenese L, Carty C, Barber L, Oberhofer K, et al. Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2017; 9(2): e1368. 28002649
  9. [11]Hicks JL, Delp SL, Schwartz MH. Can biomechanical variables predict improvement in crouch gait? Gait & Posture. 2011; 34(2): 197– 201. doi: 10.1016/j.gaitpost.2011.04.009 21616666 PMC3130107 · DOI
  10. [12]Bregman D, Van der Krogt M, De Groot V, Harlaar J, Wisse M, Collins S. The effect of ankle foot orthosis stiffness on the energy cost of walking: a simulation study. Clinical Biomechanics. 2011; 26(9): 955– 961. doi: 10.1016/j.clinbiomech.2011.05.007 21723012 · DOI
  11. [13]Uchida TK, Seth A, Pouya S, Dembia CL, Hicks JL, Delp SL. Simulating ideal assistive devices to reduce the metabolic cost of running. PLOS One. 2016; 11(9): e0163417. doi: 10.1371/journal.pone.0163417 27656901 PMC5033584 · DOI
  12. [14]Dembia CL, Silder A, Uchida TK, Hicks JL, Delp SL. Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLOS One. 2017; 12(7): e0180320. doi: 10.1371/journal.pone.0180320 28700630 PMC5507502 · DOI
  13. [15]Arones MM, Font-Llagunes JM, Fregly BJ. Exoskeleton design using subject-specific synergy-driven neuromusculoskeletal models. In: International Conference on NeuroRehabilitation. Springer; 2020. p. 145–149.
  14. [16]Rodriguez KL, Roemmich RT, Cam B, Fregly BJ, Hass CJ. Persons with Parkinson’s disease exhibit decreased neuromuscular complexity during gait. Clinical Neurophysiology. 2013; 124(7): 1390– 1397. doi: 10.1016/j.clinph.2013.02.006 23474055 PMC3679225 · DOI
  15. [17]Roemmich RT, Fregly BJ, Hass CJ. Neuromuscular complexity during gait is not responsive to medication in persons with Parkinson’s disease. Annals of Biomedical Engineering. 2014; 42(9): 1901– 1912. doi: 10.1007/s10439-014-1036-2 24866571 · DOI
  16. [19]Mahmood N, Ghorbani N, Troje NF, Pons-Moll G, Black MJ. AMASS: Archive of motion capture as surface shapes. In: Proceedings of the IEEE/CVF international conference on computer vision; 2019. p. 5442–5451.
  17. [20]Wang JM, Hamner SR, Delp SL, Koltun V. Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives. ACM Trans Graph. 2012; 31(4). doi: 10.1145/2185520.2185521 26251560 PMC4523558 · DOI
  18. [21]Wade L, Needham L, McGuigan P, Bilzon J. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ. 2022; 10: e12995. doi: 10.7717/peerj.12995 35237469 PMC8884063 · DOI
  19. [22]Topley M, Richards JG. A comparison of currently available optoelectronic motion capture systems. Journal of Biomechanics. 2020; 106: 109820. doi: 10.1016/j.jbiomech.2020.109820 32517978 · DOI
  20. [23]Bakke D, Besier T. Shape model constrained scaling improves repeatability of gait data. Journal of Biomechanics. 2020; 107: 109838. doi: 10.1016/j.jbiomech.2020.109838 32517858 · DOI
  21. [24]Dunne JJ, Uchida TK, Besier TF, Delp SL, Seth A. A marker registration method to improve joint angles computed by constrained inverse kinematics. PLOS One. 2021; 16(5): e0252425. doi: 10.1371/journal.pone.0252425 34048476 PMC8162579 · DOI
  22. [25]Sturdy JT, Silverman AK, Pickle NT. Automated optimization of residual reduction algorithm parameters in OpenSim. Journal of Biomechanics. 2022; 137: 111087. doi: 10.1016/j.jbiomech.2022.111087 35460934 · DOI
  23. [26]Bell AL, Pedersen DR, Brand RA. A comparison of the accuracy of several hip center location prediction methods. Journal of Biomechanics. 1990; 23(6): 617– 621. doi: 10.1016/0021-9290(90)90054-7 2341423 · DOI
  24. [27]Churchill DL, Incavo SJ, Johnson CC, Beynnon BD. The transepicondylar axis approximates the optimal flexion axis of the knee. Clinical Orthopaedics and Related Research®. 1998; 356: 111– 118. doi: 10.1097/00003086-199811000-00016 9917674 · DOI
  25. [29]Kirkwood RN, Culham EG, Costigan P. Radiographic and non-invasive determination of the hip joint center location: effect on hip joint moments. Clinical Biomechanics. 1999; 14(4): 227– 235. doi: 10.1016/S0268-0033(98)00073-4 10619110 · DOI
  26. [31]Leardini A, Cappozzo A, Catani F, Toksvig-Larsen S, Petitto A, Sforza V, et al. Validation of a functional method for the estimation of hip joint centre location. Journal of Biomechanics. 1999; 32(1): 99– 103. doi: 10.1016/S0021-9290(98)00148-1 10050957 · DOI · Paper page
  27. [32]Piazza SJ, Okita N, Cavanagh PR. Accuracy of the functional method of hip joint center location: effects of limited motion and varied implementation. Journal of Biomechanics. 2001; 34(7): 967– 973. doi: 10.1016/S0021-9290(01)00052-5 11410180 · DOI
  28. [33]Besier TF, Sturnieks DL, Alderson JA, Lloyd DG. Repeatability of gait data using a functional hip joint centre and a mean helical knee axis. Journal of Biomechanics. 2003; 36(8): 1159– 1168. doi: 10.1016/S0021-9290(03)00087-3 12831742 · DOI · Paper page
  29. [34]Hicks JL, Uchida TK, Seth A, Rajagopal A, Delp SL. Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. Journal of Biomechanical Engineering. 2015; 137(2). doi: 10.1115/1.4029304 25474098 PMC4321112 · DOI
  30. [35]Kadaba M, Ramakrishnan H, Wootten M, Gainey J, Gorton G, Cochran G. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. Journal of Orthopaedic Research. 1989; 7(6): 849– 860. doi: 10.1002/jor.1100070611 2795325 · DOI
  31. [36]Grood ES, Suntay WJ. A joint coordinate system for the clinical description of three-dimensional motions: application to the knee. Journal of Biomechanical Engineering. 1983; 105(2): 136– 144. doi: 10.1115/1.3138397 6865355 · DOI
  32. [37]Lu TW, O’connor J. Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints. Journal of Biomechanics. 1999; 32(2): 129– 134. doi: 10.1016/S0021-9290(98)00158-4 10052917 · DOI
  33. [38]Leardini A, Chiari L, Della Croce U, Cappozzo A. Human movement analysis using stereophotogrammetry: Part 3. Soft tissue artifact assessment and compensation. Gait & Posture. 2005; 21(2): 212– 225. doi: 10.1016/j.gaitpost.2004.05.002 15639400 · DOI · Paper page
  34. [39]Kainz H, Modenese L, Lloyd D, Maine S, Walsh H, Carty C. Joint kinematic calculation based on clinical direct kinematic versus inverse kinematic gait models. Journal of Biomechanics. 2016; 49(9): 1658– 1669. doi: 10.1016/j.jbiomech.2016.03.052 27139005 · DOI
  35. [40]Seth A, Matias R, Veloso AP, Delp SL. A biomechanical model of the scapulothoracic joint to accurately capture scapular kinematics during shoulder movements. PLOS One. 2016; 11(1): e0141028. doi: 10.1371/journal.pone.0141028 26734761 PMC4712143 · DOI
  36. [41]Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering. 2007; 54(11): 1940– 1950. doi: 10.1109/TBME.2007.901024 18018689 · DOI
  37. [42]Damsgaard M, Rasmussen J, Christensen ST, Surma E, De Zee M. Analysis of musculoskeletal systems in the AnyBody Modeling System. Simulation Modelling Practice and Theory. 2006; 14(8): 1100– 1111. doi: 10.1016/j.simpat.2006.09.001 · DOI
  38. [43]Reinbolt JA, Schutte JF, Fregly BJ, Koh BI, Haftka RT, George AD, et al. Determination of patient-specific multi-joint kinematic models through two-level optimization. Journal of Biomechanics. 2005; 38(3): 621– 626. doi: 10.1016/j.jbiomech.2004.03.031 15652563 · DOI
  39. [44]Charlton IW, Tate P, Smyth P, Roren L. Repeatability of an optimised lower body model. Gait & Posture. 2004; 20(2): 213– 221. doi: 10.1016/j.gaitpost.2003.09.004 15336293 · DOI
  40. [45]Boyd S, Boyd SP, Vandenberghe L. Convex optimization. Cambridge University Press; 2004.
  41. [46]Andersen MS, Damsgaard M, MacWilliams B. A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems. Computer Methods in Biomechanics and Biomedical Engineering. 2010; 13(2): 171– 183. doi: 10.1080/10255840903067080 19693717 · DOI
  42. [47]Kanko RM, Laende EK, Davis EM, Selbie WS, Deluzio KJ. Concurrent assessment of gait kinematics using marker-based and markerless motion capture. Journal of Biomechanics. 2021; 127: 110665. doi: 10.1016/j.jbiomech.2021.110665 34380101 · DOI
  43. [48]Pagnon D, Domalain M, Reveret L. Pose2Sim: An open-source Python package for multiview markerless kinematics. Journal of Open Source Software. 2022. doi: 10.21105/joss.04362 · DOI
  44. [49]Uhlrich SD, Falisse A, Kidziński Ł, Muccini J, Ko M, Chaudhari AS, et al. OpenCap: 3D human movement dynamics from smartphone videos. bioRxiv. 2022. doi: 10.1101/2022.07.07.499061 PMC10586693 37856442 · DOI
  45. [50]Seth A, Hicks JL, Uchida TK, Habib A, Dembia CL, Dunne JJ, et al. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLOS Computational Biology. 2018; 14(7): e1006223. doi: 10.1371/journal.pcbi.1006223 30048444 PMC6061994 · DOI
  46. [51]Todorov E, Erez T, Tassa Y. Mujoco: A physics engine for model-based control. In: 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE; 2012. p. 5026–5033.
  47. [53]Rajagopal A, Dembia CL, DeMers MS, Delp DD, Hicks JL, Delp SL. Full-body musculoskeletal model for muscle-driven simulation of human gait. IEEE Transactions on Biomedical Engineering. 2016; 63(10): 2068– 2079. doi: 10.1109/TBME.2016.2586891 27392337 PMC5507211 · DOI
  48. [54]Lai AK, Arnold AS, Wakeling JM. Why are antagonist muscles co-activated in my simulation? A musculoskeletal model for analysing human locomotor tasks. Annals of Biomedical Engineering. 2017; 45(12): 2762– 2774. doi: 10.1007/s10439-017-1920-7 28900782 PMC5989715 · DOI
  49. [57]Chang LY, Pollard NS. Constrained least-squares optimization for robust estimation of center of rotation. Journal of biomechanics. 2007; 40(6): 1392– 1400. doi: 10.1016/j.jbiomech.2006.05.010 16824530 · DOI
  50. [59]Aristidou A, Lasenby J. FABRIK: A fast, iterative solver for the Inverse Kinematics problem. Graphical Models. 2011; 73(5): 243– 260. doi: 10.1016/j.gmod.2011.05.003 · DOI
  51. [60]Kulpa R, Multon F. Fast inverse kinematics and kinetics solver for human-like figures. In: 5th IEEE-RAS International Conference on Humanoid Robots, 2005. IEEE; 2005. p. 38–43.
  52. [61]Schwartz MH, Rozumalski A. A new method for estimating joint parameters from motion data. Journal of Biomechanics. 2005; 38(1): 107– 116. doi: 10.1016/j.jbiomech.2004.03.009 15519345 · DOI
  53. [62]Hamner SR, Delp SL. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. Journal of Biomechanics. 2013; 46(4): 780– 787. doi: 10.1016/j.jbiomech.2012.11.024 23246045 PMC3979434 · DOI
  54. [63]Bianco NA, Collins SH, Liu K, Delp SL. Simulating the effect of ankle plantarflexion and inversion-eversion exoskeleton torques on center of mass kinematics during walking. bioRxiv. 2022. doi: 10.1101/2022.11.07.515398 PMC10434928 37549183 · DOI
  55. [64]Fregly BJ, Reinbolt JA, Rooney KL, Mitchell KH, Chmielewski TL. Design of patient-specific gait modifications for knee osteoarthritis rehabilitation. IEEE Transactions on Biomedical Engineering. 2007; 54(9): 1687– 1695. doi: 10.1109/TBME.2007.891934 17867361 PMC2040055 · DOI
  56. [65]Reinbolt JA, Haftka RT, Chmielewski TL, Fregly BJ. A computational framework to predict post-treatment outcome for gait-related disorders. Medical Engineering & Physics. 2008; 30(4): 434– 443. doi: 10.1016/j.medengphy.2007.05.005 17616425 · DOI
  57. [66]McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait & Posture. 2009; 29(3): 360– 369. doi: 10.1016/j.gaitpost.2008.09.003 19013070 · DOI

Cited by

Sorted by citations

Authors
Werling, K., Bianco, N. A., Raitor, M., Stingel, J., Hicks, J. L., Collins, S. H., Delp, S. L., & Liu, C. K.
Affiliations
Stanford University (US)
Corresponding author
Bianco, Nicholas A.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2023-11-30
Volume
18
Issue
11
Pages
e0295152
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0295152
PMC
PMC10688959
OpenAlex
W4389160701
Cited by
65
References
2
Citations by year
2026: 28 · 2025: 20 · 2024: 17
FWCI
6.98
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (DGE-1656518)
  • Wu Tsai Human Performance Alliance at Stanford University and the Joe and Clara Tsai Foundation
  • Stanford Bio-X (SIGF Paul Berg Interdisciplinary Biomedical Graduate Fellowship)
  • National Institutes of Health (P41-EB027060, P2C-HD101913)
  • Stanford Institute for Human-Centered Artificial Intelligence, Stanford University (Stanford-HAI-203112)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Engineering › Biomedical Engineering › Mechanics and Biomechanics Studies
Keywords
model scaling, inverse kinematics, inverse dynamics, musculoskeletal model, bi-level optimization, ground reaction force, joint kinematics, joint kinetics

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