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Ensemble of Deep Autoencoder Classifiers for Activity Recognition Based on Sensor Modalities in Smart Homes

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 902))

Abstract

Over the past few years, a particular interest has been focused toward activity recognition domain. Indeed, human activity recognition pays more attention on the extraction of relevant and discriminative features whose the implementation facilitates the seamless monitoring of functional inhabitant abilities with the involvement of sensing technology in the smart home environments. However, despite the exponential efforts made by individual standard machine learning techniques, and recently by the remarkable breakthrough of deep learning methods, designing robust activity recognition architecture remains a major challenge in term of performance, due to a high degree of uncertainty and complexity caused by inherent behavior of human actions. For the former case, the drawbacks are essentially composed of heuristic and hand-crafted methods for features extraction, shallow features learning, and learning of low amount of well-labeled data. While the latter suffers from imbalanced datasets and problematic data quality in real-life datasets. In addition, the choice of suitable sensor types is also critical for successful human activity recognition. This paper proposes an ensemble of deep classifier techniques based on hybrid sensor types composed of wearable and environment interactive sensors to improve the prediction and recognition performance of activities of daily living in smart home environments. Indeed, this ensemble is designed by combining the both automatically learned features and hand-crafted features from Denoising Stacked Autoencoders (DSAE) and Random Forest (RF) algorithm respectively. Specifically, the combination involves both the features and outputs of the two techniques using stacking learning. The use of two public benchmark datasets has enabled to evaluate our approach. Furthermore, the experimental results show the accuracy improvement of the ensembles of deep autoencoders classifiers compared to denoising stacked autoencoder networks and random forest algorithm performed individually. Hence, our approach adaptability to ubiquitous environments and its effectiveness in the recognition of human activity applications.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant No. 61471147), the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.2017037). Natural Science Foundation of Heilongjiang Province (Grant No. F2016016), the National Key Research and Development Program of China (Grant No. 2016YFC0901905).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 92, West Dazhi Street, Nangang District, Harbin, 150001, People’s Republic of China

    Serge Thomas, Mickala Bourobou & Jie Li

Authors
  1. Serge Thomas
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  2. Mickala Bourobou
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  3. Jie Li
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Contributions

Serge Thomas Mickala Bourobou proposed the work and confirmed its efficiency through the experiments. Jie Li supervised the work and directed the implementation. All authors wrote the paper and discussed the revision together.

Corresponding author

Correspondence to Jie Li .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Xidian University, Xi’an, Shaanxi, China

    Qiguang Miao

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Thomas, S., Bourobou, M., Li, J. (2018). Ensemble of Deep Autoencoder Classifiers for Activity Recognition Based on Sensor Modalities in Smart Homes. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_24

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  • DOI: https://doi.org/10.1007/978-981-13-2206-8_24

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  • Print ISBN: 978-981-13-2205-1

  • Online ISBN: 978-981-13-2206-8

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