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Human Activity Recognition Using Deep Learning-Based Approach

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Communication and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

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Abstract

There is a considerable demand for human activity recognition techniques in the area of human perception and also encompasses many other purposes like healthcare monitoring, assisted living for elders, and intelligent video surveillance. There are different approaches to machine learning that have been adapted for the purpose of activity recognition. But these techniques depend heavily on hand-crafted feature extraction which is unable to perform well when dealing with complex scenarios. Deep learning techniques have great potential for human activity recognition. In this paper, a neural network (NN) based approach for classification and evaluation of human activities has been explored. In this method, a convolutional neural network (CNN) is put together with long short-term memory (LSTM). The dataset experimented in this system is the classic Human Activity Recognition (HAR) dataset for classifying the six human activities, viz., walking, walking-upstairs, walking-downstairs, sitting, standing, and laying. Results show that the proposed model is very efficient for recognizing human activity.

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Acknowledgements

This research activity is a portion of the TEQIP Collaborative Research Scheme (CRS) project entitled, “Seamless Health Monitoring and Analysis of soldier using Machine Learning Approach” [CRS ID 1-5763896131]. The authors would like to thank NPIU, Government of India.

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Authors and Affiliations

  1. Department of Information Technology, GUIST, Guwahati, Assam, India

    Maruf Rahman & Tanuja Das

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  1. Maruf Rahman
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  2. Tanuja Das
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Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Jaipur, India

    Mukesh Kumar Gupta

  3. Birla Institute of Applied Sciences, Nainital, Uttarakhand, India

    G. S. Tomar

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Wang Lipo

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Rahman, M., Das, T. (2021). Human Activity Recognition Using Deep Learning-Based Approach. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_63

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  • DOI: https://doi.org/10.1007/978-981-16-1089-9_63

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  • Online ISBN: 978-981-16-1089-9

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