Towards Resource-Efficient Classifiers for Always-On Monitoring

  • Jonas Vlasselaer
  • Wannes MeertEmail author
  • Marian Verhelst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Emerging applications such as natural user interfaces or smart homes create a rising interest in electronic devices that have always-on sensing and monitoring capabilities. As these devices typically have limited computational resources and require battery powered operation, the challenge lies in the development of processing and classification methods that can operate under extremely scarce resource conditions. To address this challenge, we propose a two-layered computational model which enables an enhanced trade-off between computational cost and classification accuracy: The bottom layer consists of a selection of state-of-the-art classifiers, each having a different computational cost to generate the required features and to evaluate the classifier itself. For the top layer, we propose to use a Dynamic Bayesian network which allows to not only reason about the output of the various bottom-layer classifiers, but also to take into account additional information from the past to determine the present state. Furthermore, we introduce the use of the Same-Decision Probability to reason about the added value of the bottom-layer classifiers and selectively activate their computations to dynamically exploit the computational cost versus classification accuracy trade-off space. We validate our methods on the real-world SINS database, where domestic activities are recorded with an accoustic sensor network, as well as the Human Activity Recognition (HAR) benchmark dataset.



This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. Grant agreements 715037 Re-SENSE: Resource-efficient sensing through dynamic attention-scalability and 694980 SYNTH: Synthesising Inductive Data Models.


  1. 1.
    DCASE Challenge 2018: Monitoring of domestic activities based on multi-channel acoustics.
  2. 2.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2013 (2013)Google Scholar
  3. 3.
    Choi, A., Xue, Y., Darwiche, A.: Same-decision probability: a confidence measure for threshold-based decisions. Int. J. Approx. Reason. 53(9), 1415–1428 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Darwiche, A.: New advances in compiling CNF to decomposable negation normal form. In: Proceedings of ECAI, pp. 328–332 (2004)Google Scholar
  5. 5.
    Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  6. 6.
    Dekkers, G., et al.: The SINS database for detection of daily activities in a home environment using an acoustic sensor network. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE) (2017)Google Scholar
  7. 7.
    Frank, E., Hall, M.A., Witten, I.H.: The WEKA workbench. In: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)Google Scholar
  8. 8.
    Ghasemzadeh, H., Ostadabbas, S., Guenterberg, E., Pantelopoulos, A.: Wireless medical-embedded systems: a review of signal-processing techniques for classification. IEEE Sens. J. 13, 423–437 (2013)CrossRefGoogle Scholar
  9. 9.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  10. 10.
    Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2593–2600 (2014)Google Scholar
  11. 11.
    Korhonen, I., Parkka, J., Van Gils, M.: Health monitoring in the home of the future. IEEE Eng. Med. Biol. Mag. 22(3), 66–73 (2003)CrossRefGoogle Scholar
  12. 12.
    Mitra, J., Saha, D.: An efficient feature selection in classification of audio files. arXiv preprint arXiv:1404.1491 (2014)
  13. 13.
    Olascoaga, L.I.G., Meert, W., Bruyninckx, H., Verhelst, M.: Extending Naive Bayes with precision-tunable feature variables for resource-efficient sensor fusion. In: AI-IoT ECAI, pp. 23–30 (2016)Google Scholar
  14. 14.
    Oztok, U., Choi, A., Darwiche, A.: Solving PP PP-complete problems using knowledge compilation. In: Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning (2016)Google Scholar
  15. 15.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Burlington (1988)zbMATHGoogle Scholar
  16. 16.
    Piatkowski, N., Lee, S., Morik, K.: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 9–23 (2016)CrossRefGoogle Scholar
  17. 17.
    Ronao, C.A., Cho, S.B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59(C), 235–244 (2016)CrossRefGoogle Scholar
  18. 18.
    Shnayder, V., Hempstead, M., Chen, B.R., Allen, G.W., Welsh, M.: Simulating the power consumption of large-scale sensor network applications. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 188–200. ACM (2004)Google Scholar
  19. 19.
    Vlasselaer, J., Meert, W., Van den Broeck, G., De Raedt, L.: Exploiting local and repeated structure in dynamic Bayesian networks. Artif. Intell. 232, 43–53 (2016)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Xu, Z.E., Kusner, M.J., Weinberger, K.Q., Chen, M., Chapelle, O.: Classifier cascades and trees for minimizing feature evaluation cost. J. Mach. Learn. Res. 15(1), 2113–2144 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jonas Vlasselaer
    • 1
  • Wannes Meert
    • 2
    Email author
  • Marian Verhelst
    • 1
  1. 1.MICAS, Department of Electrical EngineeringKU LeuvenLeuvenBelgium
  2. 2.DTAI, Department of Computer ScienceKU LeuvenLeuvenBelgium

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