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Human Activity Recognition in a Smart Home Environment with Stacked Denoising Autoencoders

  • Aiguo Wang
  • Guilin ChenEmail author
  • Cuijuan Shang
  • Miaofei Zhang
  • Li Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Activity recognition is an important step towards automatically measuring the functional health of individuals in smart home settings. Since the inherent nature of human activities is characterized by a high degree of complexity and uncertainty, it poses a great challenge to build a robust activity recognition model. This study aims to exploit deep learning techniques to learn high-level features from the binary sensor data under the assumption that there exist discriminant latent patterns inherent in the low-level features. Specifically, we first adopt a stacked autoencoder to extract high-level features, and then integrate feature extraction and classifier training into a unified framework to obtain a jointly optimized activity recognizer. We use three benchmark datasets to evaluate our method, and investigate two different original sensor data representations. Experimental results show that the proposed method achieves better recognition rate and generalizes better across different original feature representations compared with other four competing methods.

Keywords

Activity recognition Smart homes Deep learning Autoencoder Shallow structure model 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of China (No. 61472057) and China Postdoctoral Science Foundation (No. 2016M592046).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Aiguo Wang
    • 1
    • 2
  • Guilin Chen
    • 1
    Email author
  • Cuijuan Shang
    • 1
  • Miaofei Zhang
    • 1
  • Li Liu
    • 3
  1. 1.School of Computer and Information EngineeringChuzhou UniversityChuzhouChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina
  3. 3.School of Software EngineeringChongqing UniversityChongqingChina

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