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An optimized feature selection using bio-geography optimization technique for human walking activities recognition

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Abstract

A bipedal walking robot is a kind of humanoid robot. It mimics human behavior and is devised to perform human-specific tasks. Currently, humanoid robots are not capable to walk properly like human beings. In this paper, a technique to identify different human walking activities using a human gait pattern is suggested. Human locomotion is a manifestation of a change in the joint angle of the hip, knee, and ankle. To achieve the aforementioned objective, firstly, 25 different subject’s data is collected for identification of seven different walking activities, namely, natural walk, walking on toes, walking on heels, walking upstairs, walking downstairs, sit-ups, and jogging. Next, the important features for gait activity recognition are selected using bio-geography based optimization, in which, classification accuracy is considered as a fitness function. Finally, we have explored six machine learning algorithms for the classification of gait activities, namely, support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), decision tree (DT), gradient boosting (GB), and extra tree classifier (ET). All these algorithms have been tested rigorously and achieve high accuracy of 91.64% in RF, 90.41% in SVM, 82.6% in KNN, 86.51% in DT, 88.34% in ET & 89.97% in GB respectively on our HAG dataset. The proposed technique is also validated on the WISDM data-set for comparative analysis.

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Acknowledgements

The author(s) would like to thank all the participants who have allowed us to capture the data using a wearable device. Special thanks to Human motion capturing & analysis unit of MANIT Bhopal for providing opportunity to collect data and providing the basic computing facility. The data set is also available publicly for research purposes. One can download from here: Data-set Link. The author(s) also like to express thanks to SERB, DST, Govt. of India for funding project under the schema of Early career award (ECR), DST No: ECR/2018/000203 dated on 04/06/2019.

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Authors and Affiliations

  1. MANIT Bhopal, Bhopal, India

    Vijay Bhaskar Semwal

  2. VIT Bhopal University, Bhopal, India

    Praveen Lalwani, Manas Kumar Mishra & Jasroop Singh Chadha

  3. Institute of Technology Gopeshwar, Gopeshwar, India

    Vishwanath Bijalwan

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  1. Vijay Bhaskar Semwal
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  2. Praveen Lalwani
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  4. Vishwanath Bijalwan
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  5. Jasroop Singh Chadha
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Correspondence to Praveen Lalwani.

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The author(s) also like to express thanks to SERB, DST, Govt. of India for funding the project under the schema of Early career award (ECR), DST No: ECR/2018/000203 dated 04/06/2019.

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Semwal, V.B., Lalwani, P., Mishra, M.K. et al. An optimized feature selection using bio-geography optimization technique for human walking activities recognition. Computing 103, 2893–2914 (2021). https://doi.org/10.1007/s00607-021-01008-7

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  • DOI: https://doi.org/10.1007/s00607-021-01008-7

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