Abstract
Human Gait refers to motion accomplished through the movement of hand limbs. Gait analysis is a precise investigation of the human walking pattern using sensors attached to the body for recording body movements during activities like walking on a flat surface, treadmill and running. This paper addresses the analysis of human gait based on performance using various classification techniques involving Decision Tree, Random Forest and KNN algorithms. The paper highlights the comparison of these classification models based on various parameters using the RapidMiner Studio tool. The comparison is based on performance metrics and Receiver Operating Characteristic (ROC). The results show that the Random Forest algorithm performs better in classifying normal and abnormal gait.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bakchy, S.C., Mondal, M.N.I., Ali, M.M., Hoque Sathi, A., Ray, K.C., Jannatul Ferdous, M.: Limbs and muscle movement detection using gait analysis. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, pp. 1–4 (2018). https://doi.org/10.1109/ic4me2.2018.8465598
Esquenazi, A., Talaty, M.: Gait analysis, technology and clinical applications. Phys. Med. Rehabil. 99–116 (2011)
Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-based gait recognition: a survey. IEEE Access 6, 70497–70527 (2018). https://doi.org/10.1109/ACCESS.2018.2879896
Tian, Y., Wei, L., Lu, S., Huang, T.: Free-view gait recognition. PLoS ONE 14(4), e0214389 (2019). https://doi.org/10.1371/journal.pone.0214389
Li, X., Maybank, S.J., Yan, S., Tao, D., Xu, D.: ‘Gait components and their application to gender recognition. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 38(2), 145–155 (2008). https://doi.org/10.1109/tsmcc.2007.913886
Lu, J., Wang, G., Moulin, P.: ‘Human identity and gender recognition from gait sequences with arbitrary walking directions’. IEEE Trans. Inf. Forensics Secur. 9(1), 51–61 (2014). https://doi.org/10.1109/TIFS.2013.2291969
Sudha, L.R., Bhavani, R.: An efficient spatio-temporal gait representation for gender classification. Appl. Artif. Intell. 27(1), 62–75 (2013). https://doi.org/10.1080/08839514.2013.747373
Weiss, R.J., Wretenberg, P., Stark, A., Palmblad, K., Larsson, P., Grondal, L., Brostrom, E.: Gait pattern in rheumatoid arthritis. Gait Posture 28(2), 229–234 (2008)
Saad, A., Zaarour, I., Guerin, F., Bejjani, P., Ayache, M., Lefebvre, D.: Detection of freezing of gait for Parkinson’s disease patients with multisensor device and Gaussian neural networks. Int. J. Mach. Learn. 8(3), 941–954 (2017)
Ťupa, O., et al.: Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect. Biomed. Eng. Online 14(1), 97 (2015)
Bidabadi, S.S., Murray, I., Lee, G.Y.F., Morris, S., Tan, T.: Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms. Gait and Posture 71, 234–240 (2019). ISSN 0966-6362, https://doi.org/10.1016/j.gaitpost.2019.05.010
Prajwala, T.R.: A comparative study on decision tree and random forest using R tool. Int. J. Adv. Res. Comput. Commun. Eng. 4(1), (2015)
Tahsildar, S.: Gini Index For Decision Trees. Quantinsti (2019)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)
Brownlee, J.: How to use ROC curves and precision-recall curves for classification in Python. Machine Learning Mastery (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, A., Jadhav, A., Jadhav, S., Thengade, A. (2020). Human Gait Analysis Based on Decision Tree, Random Forest and KNN Algorithms. In: Iyer, B., Rajurkar, A., Gudivada, V. (eds) Applied Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-4029-5_28
Download citation
DOI: https://doi.org/10.1007/978-981-15-4029-5_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4028-8
Online ISBN: 978-981-15-4029-5
eBook Packages: EngineeringEngineering (R0)