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A Real-Time Missing Data Recovery Method Using Recurrent Neural Network for Multiple Transmissions

  • Bor-Shing Lin
  • Yu-Syuan Lin
  • I-Jung Lee
  • Bor-Shyh Lin
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)

Abstract

Data loss and recovery is a critical issue in data transmission. Traditional data recovery methods are impractical for use in real-time systems that require multiple transmissions. To solve this problem, this study proposed a recovery method based on a recurrent neural network, which is then used to build a pre-diction model. When a data gap occurs, the missing data can be recovered immediately using the predicted value. This method distributes the calculation and can immediately recover the data gap. Through a series of experiments, this study optimized different parameters in the neural network, thus optimizing the prediction model.

Keywords

Missing data recovery Wearable technology Recurrent neural network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Taipei UniversityNew Taipei CityTaiwan
  2. 2.College of Electrical Engineering and Computer ScienceNational Taipei UniversityNew Taipei CityTaiwan
  3. 3.Institute of Imaging and Biomedical PhotonicsNational Chiao Tung UniversityTainanTaiwan

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