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A Low-Cost IMU-Based Wearable System for Precise Identification of Walk Activity Using Deep Convolutional Neural Network

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Big Data Intelligence for Smart Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 994))

Abstract

The increasing popularity of smart wearable devices can be attributed to progress in research towards human activity recognition. With the use of tiny sensing units, the different human activities of day-to-day life can be identified with a high accuracy. However, as a fitness tracking product, smart wearables are not always accurate in determining actual physical motion details. For example, recognizing walk activity may not always be possible when the monitoring device is held in hand, or kept in pockets. For precise recognition of walk activity, movement of legs needs to be monitored. However, the movement of legs while walking must be distinguished from simple leg swing activities. The present work designs an IMU based sensing system that can prevent false identification of a mimicked walk or leg swing in sitting posture as a real walk activity, using conventional and convolutional deep learning algorithms. The system shows remarkable capability of identifying an actual walk from a mimicked walk activity using CNN 95% of the time.

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Notes

  1. 1.

    https://robu.in/product/mpu6050gyrosensor2accelerometer.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Amartya Chakraborty & Nandini Mukherjee

Authors
  1. Amartya Chakraborty
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  2. Nandini Mukherjee
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Editors and Affiliations

  1. Chouaib Doukkali University, El Jadida, Morocco

    Youssef Baddi

  2. National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

    Youssef Gahi

  3. Sultan Moulay Slimane University, Settat, Morocco

    Yassine Maleh

  4. Charles Darwin University, Canberra ACT, Australia

    Mamoun Alazab

  5. Texas A&M University, San Antonio, TX, USA

    Loai Tawalbeh

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Chakraborty, A., Mukherjee, N. (2022). A Low-Cost IMU-Based Wearable System for Precise Identification of Walk Activity Using Deep Convolutional Neural Network. In: Baddi, Y., Gahi, Y., Maleh, Y., Alazab, M., Tawalbeh, L. (eds) Big Data Intelligence for Smart Applications. Studies in Computational Intelligence, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-87954-9_5

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