IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0201728.html
   My bibliography  Save this article

System for automatic gait analysis based on a single RGB-D camera

Author

Listed:
  • Ana Patrícia Rocha
  • Hugo Miguel Pereira Choupina
  • Maria do Carmo Vilas-Boas
  • José Maria Fernandes
  • João Paulo Silva Cunha

Abstract

Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable, portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 ± 25 ms and 1 ± 29 ms (walking towards the sensor), and 12 ± 23 ms and 2 ± 24 ms (walking away from the sensor). Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.

Suggested Citation

  • Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
  • Handle: RePEc:plo:pone00:0201728
    DOI: 10.1371/journal.pone.0201728
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201728
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201728&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0201728?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    2. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    3. Jochen Klucken & Jens Barth & Patrick Kugler & Johannes Schlachetzki & Thore Henze & Franz Marxreiter & Zacharias Kohl & Ralph Steidl & Joachim Hornegger & Bjoern Eskofier & Juergen Winkler, 2013. "Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liliane Pinho de Almeida & Leandro Caetano Guenka & Danielle de Oliveira Felipe & Renato Porfirio Ishii & Pedro Senna de Campos & Thomaz Nogueira Burke, 2023. "Correlation between MOVA3D, a Monocular Movement Analysis System, and Qualisys Track Manager (QTM) during Lower Limb Movements in Healthy Adults: A Preliminary Study," IJERPH, MDPI, vol. 20(17), pages 1-11, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    2. Claudio Conversano & Elise Dusseldorp, 2017. "Modeling Threshold Interaction Effects Through the Logistic Classification Trunk," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 399-426, October.
    3. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
    4. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
    5. Grabisch, Michel & Kojadinovic, Ivan & Meyer, Patrick, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," European Journal of Operational Research, Elsevier, vol. 186(2), pages 766-785, April.
    6. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    7. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    8. Karin Wolffhechel & Amanda C Hahn & Hanne Jarmer & Claire I Fisher & Benedict C Jones & Lisa M DeBruine, 2015. "Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    9. Paolo Sorino & Maria Gabriella Caruso & Giovanni Misciagna & Caterina Bonfiglio & Angelo Campanella & Antonella Mirizzi & Isabella Franco & Antonella Bianco & Claudia Buongiorno & Rosalba Liuzzi & Ann, 2020. "Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    10. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    11. Na Tang & Maoxiang Yuan & Zhijun Chen & Jian Ma & Rui Sun & Yide Yang & Quanyuan He & Xiaowei Guo & Shixiong Hu & Junhua Zhou, 2023. "Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    12. Andrea S Martinez-Vernon & James A Covington & Ramesh P Arasaradnam & Siavash Esfahani & Nicola O’Connell & Ioannis Kyrou & Richard S Savage, 2018. "An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    13. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    14. Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
    15. P. J. Zarco-Tejada & T. Poblete & C. Camino & V. Gonzalez-Dugo & R. Calderon & A. Hornero & R. Hernandez-Clemente & M. Román-Écija & M. P. Velasco-Amo & B. B. Landa & P. S. A. Beck & M. Saponari & D. , 2021. "Divergent abiotic spectral pathways unravel pathogen stress signals across species," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    16. Moro Russ A. & Härdle Wolfgang K. & Schäfer Dorothea, 2017. "Company rating with support vector machines," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 55-67, June.
    17. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    18. Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
    19. Arnout Van Messem & Andreas Christmann, 2010. "A review on consistency and robustness properties of support vector machines for heavy-tailed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 199-220, September.
    20. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0201728. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.