Human knee joint walking pattern generation using computational intelligence techniques
- 23 Downloads
Computational intelligence techniques (CITs) can be used to generate the human knee joint angle walking pattern in the sagittal plane, useful in medical rehabilitation as a specific reference of normal pattern depending on the subject’s age, mass, height and stride duration. In this paper, the knee joint angle reference curves in the sagittal plane were generated by using three different CITs: artificial neural network, extreme learning machine (ELM) and multi-output support vector regression. The gait pattern of a woman is different of the gait pattern of a man, and consequently, their knee joint angle curves are also different. Thus, it was necessary to train and test each of the three CITs for each gender. The data used by the CIT were obtained from volunteers with healthy gait and with different characteristics (gender, age, height and weight). The volunteers’ knee joint angle curves were collected by a system mainly constituted by a treadmill, two web cameras and passive marks positioned at volunteers’ joints. These gait analyses were made for five different walking speeds. It was observed that the best curves for each gender were generated using the ELM. Therefore, the ELM can be used to generate the normal knee joint angle curves expected for any person with specific characteristics (age, mass, height, stride duration), and physicians can use these specific normal curves for comparison purposes instead of using the standard knee joint angle curves of the literature which do not take into consideration the specific characteristics of the joint angle source.
KeywordsANN ELM MSVR Knee Gait Healthy
This work has been supported by Fundação para a Ciência e a Tecnologia (FCT) and project “ProjB -Diagnosis and Assisted Mobility - Centro-07-ST24-FEDER-002028” with FEDER funding, programs QREN and COMPETE. The authors also acknowledge FCT and COMPETE 2020 program for the financial support to the project “Automatic Adaptation of an Humanoid Robot Gait to Different Floor-Robot Friction Coefficients” (PTDC/EEI-AUT/5141/2014).
Compliance with ethical standards
Conflict of interest
Authors declare that they have no conflict of interest.
- 9.Pushpa Rani GAM (2010) Children abnormal GAIT classification using extreme learning machine. Glob J Comput Sci Technol 10(13):66–72Google Scholar
- 14.Ferreira PA, Ferreira JP, Crisóstomo M, Coimbra AP (2016) Low cost vision system for human gait acquisition and characterization. IEEE Int. Conf Ind Eng Eng Manag 2016:291–295Google Scholar
- 16.Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing Company, New YorkGoogle Scholar
- 17.Oberg T, Karsznia A, Oberg K (1994) Joint angle parameters in gait: reference data for normal subjects, 10–79 years of age. J Rehabil Res Dev 31(3):199–213Google Scholar
- 18.Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J (2011) Artificial neural networks ANN applied for gait classification and physiotherapy monitoring in post stroke patients. In: Suzuki K (ed) Artificial neural networks—methodological advances and biomedical applications, Chap 16. https://doi.org/10.5772/15363. ISBN 978-953-307-243-2
- 20.Gait Analysis AD plot. https://sites.google.com/site/gaitanalysisadplot/file-cabinet
- 25.Yanwei H, Dengguo W (2011) Nonlinear internal model control with inverse model based on extreme learning machine. In: Proceedings of the international conference on electric information and control engineering (ICEICE '11). pp 2391–2395Google Scholar
- 26.Lin S, Liu X, Fang J, Xu Z (2014) Is extreme learning machine feasible? a theoretical assessment (Part II). IEEE Trans Neural Netw Learn Sys, pp 21–34. https://doi.org/10.1109/TNNLS.2014.2336665
- 29.Pérez-Cruz F, Camps-Valls G, Soria-Olivas E, Pérez-Ruixo JJ, Figueiras-Vidal AR, Artés-Rodríguez A (2002) Multi-dimensional function approximation and regression estimation. In: Dorronsoro JR (ed) Artificial neural networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin. https://doi.org/10.1007/3-540-46084-5_123 Google Scholar
- 30.Xu Y, Lv X, Xi W (2012) A weighted multi-output support vector regression and its application. J Comput Inf Syst 8(9):3807–3814Google Scholar
- 31.Shetty S, Rao YS (2016) SVM based machine learning approach to identify Parkinson’s disease using gait analysis. In: Inventive Computation Technologies (ICICT), international conference, vol 2. IEEEGoogle Scholar
- 34.Ribeiro B, Lopes N (2013) Extreme learning classifier with deep concepts. Lecture Notes on Computer Science (including Subseries Lecture Notes Artificial Intelligence Lecture Notes on Bioinformatics), vol 8258 LNCS, no. PART 1, pp 182–189Google Scholar
- 35.Gomes A, Araújo N, Meneghesso L, Ricardo A, Leite M (2005) System for kinematical analysis of the human gait based on videogrammetry Metodologia Modelo biomecânico. Fisioter e Pesqui 11:3–10Google Scholar