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)


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.


Activity recognition Smart homes Deep learning Autoencoder Shallow structure model 



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


  1. 1.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27, 32–38 (2010)CrossRefGoogle Scholar
  3. 3.
    Ordóñez, F., de Toledo, P., Sanchis, A.: Sensor-based Bayesian detection of anomalous living patterns in a home setting. Pers. Ubiquit. Comput. 19, 259–270 (2015)CrossRefGoogle Scholar
  4. 4.
    Suryadevara, N.K., Mukhopadhyay, S.C.: Determining wellness through an ambient assisted living environment. IEEE Intell. Syst. 29, 30–37 (2014)CrossRefGoogle Scholar
  5. 5.
    Liu, L., Peng, Y.X., Wang, S., Huang, Z.G., Liu, M.: Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors. Inf. Sci. 340–341, 41–57 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cook, D.J., Krishnan, N.C., Rashidi, P.: Activity discovery and activity recognition: a new partnership. IEEE Trans. Cybern. 43, 820–828 (2013)CrossRefGoogle Scholar
  7. 7.
    Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138–154 (2014)CrossRefGoogle Scholar
  8. 8.
    Tapia, E., Intille, S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 11th IEEE International Symposium on Wearable Computers, pp. 37–40. IEEE Press, New York (2007)Google Scholar
  9. 9.
    Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hähnel, D.: Inferring activities from interactions with objects. IEEE Pervas. Comput. 3, 50–57 (2004)CrossRefGoogle Scholar
  10. 10.
    van Kasteren, T., Englebienne, G., Kröse, B.: An activity monitoring system for elderly care using generative and discriminative models. Pers. Ubiquit. Comput. 14, 489–498 (2010)CrossRefGoogle Scholar
  11. 11.
    Liu, L., Peng, Y.X., Huang, Z.G., Liu, M.: Sensor-based human activity recognition system with a multilayered model using time series shapelets. Knowl. Based Syst. 90, 138–152 (2015)CrossRefGoogle Scholar
  12. 12.
    Plötz, T., Hammerla, N.Y., Olivier, P.: Feature learning for activity recognition in ubiquitous computing. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1729–1734. AAAI Press, California (2011)Google Scholar
  13. 13.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C 42, 790–808 (2012)CrossRefGoogle Scholar
  15. 15.
    Figo, D., Diniz, P.C., Ferreira, D.R., Cardoso, J.M.: Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquit. Comput. 14, 645–662 (2010)CrossRefGoogle Scholar
  16. 16.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Reiss, A., Hendeby, G., Stricker, D.: A competitive approach for human activity recognition on smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Belgium, pp. 455–460 (2013)Google Scholar
  18. 18.
    Wang, A.G., Chen, G.L., Yang, J., Zhao, S.H., Chang, C.Y.: A comparative study on human activity recognition using inertial sensors in a smartphone. IEEE Sens. J. 16, 4566–4578 (2016)CrossRefGoogle Scholar
  19. 19.
    Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: 8th International Conference on Intelligent Environments, pp. 214–221. IEEE Press, New York (2012)Google Scholar
  20. 20.
    Kim, S.C., Jeong, Y.S., Park, S.O.: RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments. Pers. Ubiquit. Comput. 17, 1699–1707 (2013)CrossRefGoogle Scholar
  21. 21.
    Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9, ACM Press, New York (2008)Google Scholar
  22. 22.
    Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biol. 14, 274–283 (2010)CrossRefGoogle Scholar
  23. 23.
    Wilson, D.H., Atkeson, C.G.: Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Bhattacharya, S., Nurmi, P., Hammerla, N., Plötz, T.: Using unlabeled data in a sparse coding framework for human activity recognition. Pervasive Mob. Comput. 15, 242–262 (2014)CrossRefGoogle Scholar
  25. 25.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM Press, New York (2008)Google Scholar
  26. 26.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, pp. 153–160. MIT Press, Cambridge (2007)Google Scholar
  29. 29.
    van Kasteren, T., Englebienne, G., Kröse, J.A.B.: Human activity recognition from wireless sensor network data: benchmark and software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 165–186. Atlantis, Amsterdam (2011)CrossRefGoogle Scholar

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

Personalised recommendations