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Human Body Posture Recognition Using Wearable Devices

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11731))

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Abstract

Recently, the activities of elder people are monitored to support them live independently and safely, where the embedded hardware systems such as wearable devices are widely used. It is a research challenge to deploy deep learning algorithms on embedded devices to recognize the human activities, with the hardware constraints of limited computing resources and low power consumption. In this paper, human body posture recognition methods are proposed for the wearable embedded systems, where back propagation neural network (BPNN) and binary neural network (BNN) are employed to classify the human body postures. The BNN quantizes the synaptic weights and activation values to +1 or −1 based on the BPNN, and is able to achieve a good trade-off between the performance and cost for the embedded systems. In the experiments, the proposed methods are deployed on embedded device of Raspberry Pi 3 for real application of body postures recognition. Results show that compared with BPNN, the BNN can achieve a better trade-off between classification accuracy and cost including required computing resource, power consumption and processing time, e.g. it uses 85.29% less memory, 8.86% less power consumption, and has 5.19% faster classification speed. Therefore, the BNN is more suitable for deployment to resource constrained embedded hardware devices, which is of great significance for the application of human body posture recognition using wearable devices.

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Acknowledgments

This research was partially supported by the National Natural Science Foundation of China under Grant 61603104, the Guangxi Natural Science Foundation under Grants 2017GXNSFAA198180 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Program of Guangxi Higher Education under Grant F-KA16035, and 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program.

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Authors and Affiliations

  1. School of Electronic Engineering, Guangxi Normal University, Guilin, 541004, China

    Junxiu Liu, Mingxing Li, Yuling Luo & Senhui Qiu

  2. School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, BT48 7JL, Northern Ireland, UK

    Su Yang

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  1. Junxiu Liu
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  2. Mingxing Li
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  3. Yuling Luo
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  4. Su Yang
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  5. Senhui Qiu
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Corresponding author

Correspondence to Yuling Luo .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Liu, J., Li, M., Luo, Y., Yang, S., Qiu, S. (2019). Human Body Posture Recognition Using Wearable Devices. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_33

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30492-8

  • Online ISBN: 978-3-030-30493-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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