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Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Author

Listed:
  • Negar Golestani

    (University of Southern California)

  • Mahta Moghaddam

    (University of Southern California)

Abstract

Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.

Suggested Citation

  • Negar Golestani & Mahta Moghaddam, 2020. "Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15086-2
    DOI: 10.1038/s41467-020-15086-2
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    Cited by:

    1. Amirhossein Hajiaghajani & Patrick Rwei & Amir Hosein Afandizadeh Zargari & Alberto Ranier Escobar & Fadi Kurdahi & Michelle Khine & Peter Tseng, 2023. "Amphibious epidermal area networks for uninterrupted wireless data and power transfer," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Ahmad Jalal & Mouazma Batool & Kibum Kim, 2020. "Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    3. Ahmad Jalal & Israr Akhtar & Kibum Kim, 2020. "Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing," Sustainability, MDPI, vol. 12(23), pages 1-24, November.

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