Biomechanics dataset

Information of public available data sets for biomechanics.
Please check the license of invidual data set before using it.

[Kinematics + forces + EMG] data sets

With C3D files

• CAMS-Knee: https://cams-knee.orthoload.com/
• Perera, C. K., Hussain, Z., Khant, M., Gopalai, A. A., Gouwanda, D., & Ahmad, S. A. (2024). A Motion Capture Dataset on Human Sitting to Walking Transitions. Scientific Data, 11(1), 878. https://doi.org/10.1038/s41597-024-03740-z (data)
• Baček, T., Sun, M., Liu, H., Chen, Z., Manzie, C., Burdet, E., ... & Tan, Y. (2024). A biomechanics and energetics dataset of neurotypical adults walking with and without kinematic constraints. Scientific Data, 11(1), 646. https://doi.org/10.1038/s41597-024-03444-4 (data)
• Scherpereel, K., Molinaro, D., Inan, O., Shepherd, M., & Young, A. (2023). A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities. Scientific Data, 10(1), 924. https://doi.org/10.1038/s41597-023-02840-6 (data)
• Moreira, L., Figueiredo, J., Fonseca, P., Vilas-Boas, J. P., & Santos, C. P. (2021). Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds. Scientific Data, 8(1), 1-11. https://doi.org/10.1038/s41597-021-00881-3 (data)
• Schreiber, C., & Moissenet, F. (2019). A multimodal dataset of human gait at different walking speeds established on injury-free adult participants. Scientific data, 6(1), 1-7. https://doi.org/10.1038/s41597-019-0124-4 (data)
• Fregly, B. J., Besier, T. F., Lloyd, D. G., Delp, S. L., Banks, S. A., Pandy, M. G., & D'lima, D. D. (2012). Grand challenge competition to predict in vivo knee loads. Journal of Orthopaedic Research, 30(4), 503-513. https://simtk.org/projects/kneeloads

Other file formats

• Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O. W., & Li, G. (2023). Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition. Scientific Data, 10(1), 358. https://doi.org/10.1038/s41597-023-02263-3 (data)
• Averta, G., Barontini, F., Catrambone, V., Haddadin, S., Handjaras, G., Held, J. P., ... & Bianchi, M. (2021). U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions. GigaScience, 10(6), giab043. https://doi.org/10.1093/gigascience/giab043
• Camargo, J., Ramanathan, A., Flanagan, W., & Young, A. (2021). A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions. Journal of Biomechanics, 119, 110320. https://doi.org/10.1016/j.jbiomech.2021.110320
• R Macaluso, K Embry, D Villarreal, R Gregg, "Human Leg Kinematics, Kinetics, and EMG during Phase-Shifting Perturbations at Varying Inclines", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/12hp-e249
• Lencioni, T., Carpinella, I., Rabuffetti, M., Marzegan, A., & Ferrarin, M. (2019). Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks. Scientific data, 6(1), 1-10. https://doi.org/10.1038/s41597-019-0323-z (data)
• K. Embry, D. Villarreal, R. Macaluso, R. Gregg, "The Effect of Walking Incline and Speed on Human Leg Kinematics, Kinetics, and EMG", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/gk32-e868
• Wang, W., Li, K., Yue, S., Yin, C., & Wei, N. (2017). Associations between lower-limb muscle activation and knee flexion in post-stroke individuals: A study on the stance-to-swing phases of gait. PLoS One, 12(9), e0183865. https://doi.org/10.1371/journal.pone.0183865

[Kinematics + forces/EMG] data sets

With C3D files

• Orthoload: https://orthoload.com/
• Laboratory for Movement Biomechanics, ETH Zürich: https://movement.ethz.ch/data-repository.html
• University of Pennsylvania SIG Center for Computer Graphics Multi Modal Motion Capture Library: https://fling.seas.upenn.edu/~mocap/
• Riglet, L., Delphin, C., Claquesin, L., Orliac, B., Ornetti, P., Laroche, D., & Gueugnon, M. (2024). 3D motion analysis dataset of healthy young adult volunteers walking and running on overground and treadmill. Scientific Data, 11(1), 556. https://doi.org/10.1038/s41597-024-03420-y (data)
• Van Criekinge, T., Saeys, W., Truijen, S., Vereeck, L., Sloot, L. H., & Hallemans, A. (2023). A full-body motion capture gait dataset of 138 able-bodied adults across the life span and 50 stroke survivors. Scientific data, 10(1), 852. https://doi.org/10.1038/s41597-023-02767-y (data)
• van der Zee, T. J., Mundinger, E. M., & Kuo, A. D. (2022). A biomechanics dataset of healthy human walking at various speeds, step lengths and step widths. Scientific data, 9(1), 704. https://doi.org/10.1038/s41597-022-01817-1 (data)
• Szczęsna, A., Błaszczyszyn, M., & Pawlyta, M. (2021). Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes. Scientific Data, 8(1), 1-12. https://doi.org/10.1038/s41597-021-00801-5 (data)
• Liang, P., Kwong, W. H., Sidarta, A., Yap, C. K., Tan, W. K., Lim, L. S., ... & Ang, W. T. (2020). An Asian-centric human movement database capturing activities of daily living. Scientific Data, 7(1), 1-13. https://doi.org/10.1038/s41597-020-00627-7 (data)
• Hood, S., Ishmael, M. K., Gunnell, A., Foreman, K. B., & Lenzi, T. (2020). A kinematic and kinetic dataset of 18 above-knee amputees walking at various speeds. Scientific Data, 7(1), 1-8. https://doi.org/10.1038/s41597-020-0494-7 (data)
• Horst, F., Lapuschkin, S., Samek, W., Müller, K. R., & Schöllhorn, W. I. (2019). Explaining the unique nature of individual gait patterns with deep learning. Scientific reports, 9(1), 1-13. https://doi.org/10.1038/s41598-019-38748-8 (data)
• Matijevich, E. S., Branscombe, L. M., Scott, L. R., & Zelik, K. E. (2019). Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: Implications for science, sport and wearable tech. PloS one, 14(1), e0210000. https://doi.org/10.1371/journal.pone.0210000 (data)
• Honert, E. C., & Zelik, K. E. (2019). Foot and shoe responsible for majority of soft tissue work in early stance of walking. Human movement science, 64, 191-202. https://doi.org/10.1016/j.humov.2019.01.008 (data)
• Caravan A, Scheffey JO, Briend SJ, Boddy KJ. 2018. Surface electromyographic analysis of differential effects in kettlebell carries for the serratus anterior muscles. PeerJ 6:e5044 https://doi.org/10.7717/peerj.5044 (data)
• Deborah A. Jehu, Hiram Cantu, Allen Hill, Caroline Paquette, Julie N. Cote, & Julie Nantel. (2018). Standing repetitive pointing task in individuals with and without Parkinson's disease (Version v1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1199647
• Ozkaya, G., Jung, H. R., Jeong, I. S., Choi, M. R., Shin, M. Y., Lin, X., ... & Lee, K. K. (2018). Three-dimensional motion capture data during repetitive overarm throwing practice. Scientific data, 5(1), 1-6. https://doi.org/10.1038/sdata.2018.272 (data)
• Lunn, David and Chapman, Graham and Redmond, Anthony (2018) Motion analysis in total joint replacement patients: Data Release 1. University of Leeds. [Dataset] https://doi.org/10.5518/345
• Fukuchi, Claudiane; Fukuchi, Reginaldo; Duarte, Marcos (2018): A public data set of overground and treadmill walking kinematics and kinetics of healthy individuals. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5722711.v4
• Fukuchi, Reginaldo; Fukuchi, Claudiane; Duarte, Marcos (2017): A public data set of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics. figshare. Dataset. https://doi.org/10.6084/m9.figshare.4543435.v4

Other file formats

• Dimitrov, H., Bull, A. M., & Farina, D. (2023). High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities. Scientific Data, 10(1), 789. https://doi.org/10.1038/s41597-023-02679-x (data)
• Schulte, R. V., Prinsen, E. C., Schaake, L., Paassen, R. P., Zondag, M., van Staveren, E. S., ... & Buurke, J. H. (2023). Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects. Scientific Data, 10(1), 461. https://doi.org/10.1038/s41597-023-02341-6 (data)
• Jarque-Bou, N. J., Atzori, M., & Müller, H. (2020). A large calibrated database of hand movements and grasps kinematics. Scientific data, 7(1), 1-10. https://doi.org/10.1038/s41597-019-0349-2
• Goršič, Maja, Dai, Boyi, & Novak, Domen. (2020). Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3941377
• Wu, Amy R; Simpson, Cole S; van Asseldonk, Edwin HF; van der Kooij, Herman; Ijspeert, Auke J, 2019, "Mechanics of very slow human walking", https://doi.org/10.5683/SP2/EMQLLE, Scholars Portal Dataverse, V2, UNF:6:1F60fhDr/grQicUsGV68gQ== [fileUNF]
• Matran-Fernandez, A., Rodríguez Martínez, I.J., Poli, R. et al. SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements. Sci Data 6, 186 (2019). https://doi.org/10.1038/s41597-019-0200-9 (data)
• Krasoulis, Agamemnon; Vijayakumar, Sethu; Nazarpour, K. (2019): EMG and data glove dataset for dexterous myoelectric control. Newcastle University. Dataset. https://doi.org/10.2540xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx5/data.ncl.9577598.v1
• Donati, Elisa. (2019). EMG from forearm datasets for hand gestures recognition [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3194792
• Jarque-Bou, Nestor, Vergara, Margarita, Sancho-Bru, Joaquín L., Gracia-Ibáñez, Verónica, & Roda-Sales, Alba. (2019). A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3469380
• De Graaf, Myriam Lauren, Hubert, Juul, Houdijk, Han, & Bruijn, Sjoerd M. (2019). Data: Influence of Arm Swing on Cost of Transport during Walking [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2671651
• Mohammadreza Mahaki, Sjoerd M. Bruijn, & Jaap H. van Dieën. (2019). Data: The effect of external lateral stabilization on the use of foot placement to control mediolateral stability in walking and running [Data set]. http://dx.doi.org/10.7717/peerj.7939. Zenodo. http://doi.org/10.5281/zenodo.3468501
• Hu, B., Rouse, E., & Hargrove, L. (2018). Benchmark datasets for bilateral lower-limb neuromechanical signals from wearable sensors during unassisted locomotion in able-bodied individuals. Frontiers in Robotics and AI, 5, 14. https://doi.org/10.3389/frobt.2018.00014 (data)
• Mohr, Maurice; Schön, Tanja; von Tscharner, Vinzenz; Nigg, Benno (2018), “Data for: Intermuscular Coherence between Surface EMG Signals recorded with Monopolar and Bipolar EMG Systems during Squatting”, Mendeley Data, v1 http://dx.doi.org/10.17632/37wyv32y8j.1
• HuGaDB: https://github.com/romanchereshnev/HuGaDB

[Kinematics only] data sets

With C3D files

• KIT Whole-Body Human Motion Database: https://motion-database.humanoids.kit.edu/
• SFU Motion Capture Database: http://mocap.cs.sfu.ca/
• Mocap Database HDM05: http://resources.mpi-inf.mpg.de/HDM05/index.html
• UMONS-TAICHI: https://github.com/numediart/UMONS-TAICHI (data)
• Berkeley Multimodal Human Action Database (MHAD): https://tele-immersion.citris-uc.org/berkeley_mhad
• Dance Motion Capture Database: http://dancedb.eu/
• The UMPM Benchmark: http://www2.projects.science.uu.nl/umpm/
• Cologne Motion Capture Database: https://mocap.web.th-koeln.de/
• AnDyDataset: https://andydataset.loria.fr/
• Szczęsna, A., Błaszczyszyn, M., & Pawlyta, M. (2021). Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes. Scientific Data, 8(1), 1-12. https://doi.org/10.1038/s41597-021-00801-5 (data)
• Rengifo Rodas, Carlos Felipe; Caicedo Rodríguez, Pablo Eduardo ; Rodriguez Cheu, Luis Eduardo; Sierra Arevalo, Wilson Alexander; Gómez Guevara, Maria Catalina (2020), “Dataset for gait analysis and assessment of fall risk for older adults”, Mendeley Data, v1 http://dx.doi.org/10.17632/xzv6bjm9x2.1
• Boddy, K. J., Marsh, J. A., Caravan, A., Lindley, K. E., Scheffey, J. O., & O’Connell, M. E. (2019). Exploring wearable sensors as an alternative to marker-based motion capture in the pitching delivery. PeerJ, 7, e6365. https://doi.org/10.7717/peerj.6365
• Maurice, Pauline, Malaisé, Adrien, Ivaldi, Serena, Rochel, Olivier, Amiot, Clelie, Paris, Nicolas, … Fritzsche, Lars. (2019). AndyData-lab-onePerson (Version 2) [Data set]. The International Journal of Robotics Research. Zenodo. http://doi.org/10.5281/zenodo.3254403
• Malaisé, Adrien, Maurice, Pauline, Ivaldi, Serena, Colas, Francis, & Rochel, Olivier. (2018). AndyData-lab-onePersonWithShoes (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1472122
• Maurice, Pauline, Camernik, Jernej, Gorjan, Dasa, Babic, Jan, Schirrmeister, Benjamin, Bornmann, Jonas, … Pucci, Daniele. (2018). AndyData-lab-onePersonWithExoskeleton (Version 1) [Data set]. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Zenodo. http://doi.org/10.5281/zenodo.1472214
• Drew, Alex J., Izykowski, Morgan T., Bachus, Kent N., Henninger, Heath B., & Foreman, K. Bo. (2017). Transhumeral Loading During Advanced Upper Extremity Activities of Daily Living [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1040453
• Huang, Yu-Fen; Moran, Nikki; Coleman, Simon. (2017). Orchestral conducting motion capture data, 2015 [dataset]. Reid School of Music, University of Edinburgh; Institute for Sport, Physical Education and Health Sciences, University of Edinburgh. https://doi.org/10.7488/ds/2223.

Other file formats

• HuMoD Database: https://www.sim.informatik.tu-darmstadt.de/res/ds/humod/
• Emotional Body Motion Database: http://ebmdb.tuebingen.mpg.de/index.php
• Human 3.6M: http://vision.imar.ro/human3.6m/
• Interaction Database CGVU @ The University of Edinburgh: http://www.ipab.inf.ed.ac.uk/cgvu/InteractionDatabase/interactiondb.html
• MoCap ULL Project: http://atrae.webs.ull.es/capturadormovimiento/
• HumanEva: http://humaneva.is.tue.mpg.de/
• Ferber, R., Brett, A., Fukuchi, R. K., Hettinga, B., & Osis, S. T. (2024). A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running. Scientific Data, 11(1), 1232. https://www.nature.com/articles/s41597-024-04011-7 (data)
• Hanisch, S., Pogrzeba, L., Muschter, E., Li, S. C., & Strufe, T. (2024). A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults. Scientific Data, 11(1), 1209. https://doi.org/10.1038/s41597-024-04020-6 (data)
• Zhou, L., Fischer, E., Brahms, C. M., Granacher, U., & Arnrich, B. (2023). Duo-gait: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Scientific Data, 10(1), 543. https://doi.org/10.1038/s41597-023-02391-w (data)
• Wiles, T. M., Mangalam, M., Sommerfeld, J. H., Kim, S. K., Brink, K. J., Charles, A. E., ... & Likens, A. D. (2023). NONAN GaitPrint: An IMU gait database of healthy young adults. Scientific Data, 10(1), 867. https://doi.org/10.1038/s41597-023-02704-z (data)
• Mehdizadeh, S., Nabavi, H., Sabo, A., Arora, T., Iaboni, A., & Taati, B. (2022). The toronto older adults gait archive: video and 3d inertial motion capture data of older adults’ walking. Scientific data, 9(1), 398. https://doi.org/10.1038/s41597-022-01495-z (data)
• Nicora, E., Goyal, G., Noceti, N., Vignolo, A., Sciutti, A., & Odone, F. (2020). The MoCA dataset, kinematic and multi-view visual streams of fine-grained cooking actions. Scientific Data, 7(1), 1-15. https://doi.org/10.1038/s41597-020-00776-9 (data)
• Luo, Yue; Coppola, Sarah; Dixon, Philippe; Li, Song; Dennerlein, Jack; Hu, Boyi (2020): A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4892463.v1
• Schwarz, Anne, Bhagubai, Miguel M. C., Wolterink Gerjan, Held, Jeremia P. O., Luft, Andreas R., & Veltink, Peter H. (2020). Kinematics of reach-to-grasp and displacement after stroke [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3930752
• Schwarz, Anne, Held, Jeremia P. O., & Luft, Andreas R. (2020). Post-stroke upper limb kinematics of a set of daily living tasks (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3713449
• Vieten, Manfred, & Weich, Christian. (2019). The kinematics of cyclic human movement [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3518415
• NIU, Haijun; SHEN, Fei; GAO, Xing; Fan, Yubo; WANG, Li; MA, Yingnan (2018), “Data for: Dynamic walking stability of elderly people with various BMIs”, Mendeley Data, v1 http://dx.doi.org/10.17632/yp2s4rdjjk.1

[Other] data sets

• AMASS: https://amass.is.tue.mpg.de/
• WhoLoDance: http://www.wholodance.eu/
• AIST Gait Database 2019: https://unit.aist.go.jp/harc/ExPART/GDB2019_e.html
• Malešević, N., Olsson, A., Sager, P., Andersson, E., Cipriani, C., Controzzi, M., ... & Antfolk, C. (2021). A database of high-density surface electromyogram signals comprising 65 isometric hand gestures. Scientific Data, 8(1), 1-10. https://doi.org/10.1038/s41597-021-00843-9
• Brokoslaw Laschowski, Reza Sharif Razavian, John McPhee, "Measurement and Simulation of Human Sitting and Standing Movement Biomechanics ", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/ky0b-fe94. Accessed: Aug. 06, 2020.
• Ilaria Mileti, Aurora Serra, Nerses Wolf, Victor Munoz-Martel, Antonis Ekizos, Eduardo Palermo, … Alessandro Santuz. (2020). Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion (Version 1.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3932768
• Santuz, Alessandro, Ekizos, Antonis, Kunimasa, Yoko, Kijima, Kota, Ishikawa, Masaki, & Arampatzis, Adamantios. (2020). Lower complexity of motor primitives ensures robust control of high-speed human locomotion (Version 1.2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3785077
• Santuz, Alessandro, Ekizos, Antonis, Janshen, Lars, Mersmann, Falk, Bohm, Sebastian, Baltzopoulos, Vasilios, & Arampatzis, Adamantios. (2020). Modular control of human movement during running: an open access data set (Version 1.3.0) [Data set]. Frontiers in Physiology | Exercise Physiology. Zenodo. http://doi.org/10.5281/zenodo.3785076
• Santuz, Alessandro, Brüll, Leon, Ekizos, Antonis, Schroll, Arno, Eckardt, Nils, Kibele, Armin, … Arampatzis, Adamantios. (2020). Neuromotor dynamics of human locomotion in challenging settings (Version 2.3.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3785065
• Wang, Huawei, & van den Bogert, Antonie. (2020). Standing Balance Experiment with Long Duration Random Pulses Perturbation (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3819630
• Horsak, Brian; Slijepcevic, Djordje; Raberger, Anna-Maria; Schwab, Caterine; Worisch, Marianne; Zeppelzauer, Matthias (2020): GaitRec: A large-scale ground reaction force dataset of healthy and impaired gait. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4788012.v1
• Hug, Francois; Vogel, Clément; Tucker, Kylie; Dorel, Sylvain; Deschamps, Thibault; Carpentier, Eric le; et al. (2019): Individuals have unique muscle activation signatures as revealed during gait and pedaling. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8273774.v3
• Mohr, Maurice; von Tscharner, Vinzenz; Emery, Carolyn ; Nigg, Benno (2019), “Data for: Surface EMG muscle activation patterns of the lower extremities during gait in individuals with and without a knee injury history”, Mendeley Data, v1 http://dx.doi.org/10.17632/f2fv7gb577.1
• Kerkman, Jennifer, Daffertshofer, Andreas, & Boonstra, Tjeerd. (2018). Muscle activity, ground reaction forces and pointing performance during postural control tasks in healthy adults [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1185196
• Liew, Bernard; Yu, Yue (Ryan); Cescon, Corrado; Barbero, Marco; Falla, Deborah (2018), “HDsEMG data analysis using R-INLA”, Mendeley Data, v1 http://dx.doi.org/10.17632/8hdb83vdvt.1

C3D libraries

• C3Dserver
• BTK (conda-forge, PyPI)
• EZC3D

C3D applications

Python

• PyC3Dserver

MATLAB

• MOtoNMS

References

• C3D.ORG