Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors

  • Yongmou Li
  • Dianxi Shi
  • Bo Ding
  • Dongbo Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)


Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from non-adaptability to the particular dataset. To alleviate the problems of hand-crafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and PCA. We evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework.


human activity recognition unsupervised feature learning sparse auto-encoder denoising auto-encoder 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yongmou Li
    • 1
  • Dianxi Shi
    • 1
  • Bo Ding
    • 1
  • Dongbo Liu
    • 1
  1. 1.National Key Laboratory for Parallel and Distributed Processing, College of ComputerNational University of Defense TechnologyChangshaChina

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