Skip to main content
SpringerLink
Log in

A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors

  • Conference paper
  • First Online:
Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10935))

Abstract

Accelerometers are become ubiquitous and available in several devices such as smartphones, smartwaches, fitness trackers, and wearable devices. Accelerometers are increasingly used to monitor human activities of daily living in different contexts such as monitoring activities of persons with cognitive deficits in smart homes, and monitoring physical and fitness activities. Activity recognition is the most important core component in monitoring applications. Activity recognition algorithms require substantial amount of labeled data to produce satisfactory results under diverse circumstances. Several methods have been proposed for activity recognition from accelerometer data. However, very little work has been done on identifying connections and relationships between existing labeled datasets to perform transfer learning for new datasets. In this paper, we investigate deep learning based transfer learning algorithm based on convolutional neural networks (CNNs) that takes advantage of learned representations of activities of daily living from one dataset to recognize these activities in different other datasets characterized by different features including sensor modality, sampling rate, activity duration and environment. We experimentally validated our proposed algorithm on several existing datasets and demonstrated its performance and suitability for activity recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cook, D., Feuz, K.D., Krishnan, N.C.: Transfer learning for activity recognition: a survey. Knowl. Inf. Syst. 36(3), 537–556 (2013)

    Article  Google Scholar 

  2. Chikhaoui, B., Ye, B., Mihailidis, A.: Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. J. Ambient Intell. Hum. Comput. 1–15 (2017)

    Google Scholar 

  3. Chen, W.-H., Cho, P.-C., Jiang, Y.-L.: Activity recognition using transfer learning. Sens. Mater. 29(7), 897–904 (2017)

    Google Scholar 

  4. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  5. Chikhaoui, B., Gouineau, F.: Towards automatic feature extraction for activity recognition from wearable sensors: a deep learning approach. In: 2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, 18–21 November 2017, pp. 693–702 (2017)

    Google Scholar 

  6. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, ICML 2014, pages II–1764–II–1772. JMLR.org (2014)

    Google Scholar 

  7. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoring: a survey. CoRR, abs/1612.07640 (2016)

    Google Scholar 

  8. Gokul, V., Kannan, P., Kumar, S., Jacob, S.G.: Deep Q-learning for home automation. Int. J. Comput. Appl. 152(6), 1–5 (2016)

    Google Scholar 

  9. Kim, J., Mo, Y.J., Lee, W., Nyang, D.: Dynamic security-level maximization for stabilized parallel deep learning architectures in surveillance applications. In: 2017 IEEE Symposium on Privacy-Aware Computing (PAC), pp. 192–193 (2017)

    Google Scholar 

  10. Morales, F.J.O., Roggen, D.: Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers, ISWC 2016, Heidelberg, Germany, 12–16 September 2016, pp. 92–99 (2016)

    Google Scholar 

  11. Morales, F.J.O., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)

    Article  Google Scholar 

  12. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8–13 December 2014, Montreal, Quebec, Canada, pp. 3320–3328 (2014)

    Google Scholar 

  13. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. CoRR, abs/1707.03502 (2017)

    Google Scholar 

  14. Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 1533–1540 (2016)

    Google Scholar 

  15. Sargano, A.B., Wang, X., Angelov, P., Habib, Z.: Human action recognition using transfer learning with deep representations. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 463–469 (2017)

    Google Scholar 

  16. Kunze, J., Kirsch, L., Kurenkov, I., Krug, A., Johannsmeier, J., Stober, S.: Transfer learning for speech recognition on a budget. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, Rep4NLP@ACL 2017, Vancouver, Canada, 3 August 2017, pp. 168–177 (2017)

    Google Scholar 

  17. Zheng, V.W., Hu, D.H., Yang, Q.: Cross-domain activity recognition. In: Proceedings of the 11th International Conference on Ubiquitous Computing, UbiComp 2009, pp. 61–70 (2009)

    Google Scholar 

  18. Ordóñez, F.J., Englebienne, G., de Toledo, P., van Kasteren, T., Sanchis, A., Kröse, B.: In-home activity recognition: Bayesian inference for hidden Markov models. IEEE Pervasive Comput. 13(3), 67–75 (2014)

    Article  Google Scholar 

  19. Rashidi, P., Cook, D.J.: Activity recognition based on home to home transfer learning. In: Proceedings of the 5th AAAI Conference on Plan, Activity, and Intent Recognition, AAAIWS 2010–05, pp. 45–52 (2010)

    Google Scholar 

  20. van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Transferring knowledge of activity recognition across sensor networks. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 283–300. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12654-3_17

    Chapter  Google Scholar 

  21. Kurz, M., Hölzl, G., Ferscha, A., Calatroni, A., Roggen, D., Tröster, G.: Real-time transfer and evaluation of activity recognition capabilities in an opportunistic system. In: Adaptive, pp. 73–78 (2011)

    Google Scholar 

  22. Calatroni, A., Roggen, D., Tröster, G.: Automatic transfer of activity recognition capabilities between body-worn motion sensors: training newcomers to recognize locomotion. In: Eighth International Conference on Networked Sensing Systems (INSS 2011) (2011)

    Google Scholar 

  23. Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 3–9 (2016)

    Article  Google Scholar 

  24. Bhattacharya, S., Nurmi, P., Hammerla, N., Plötz, T.: Using unlabeled data in a sparse-coding framework for human activity recognition. Perv. Mob. Comput. 15(C), 242–262 (2014)

    Article  Google Scholar 

  25. Ha, S., Yun, J.M., Choi, S.: Multi-modal convolutional neural networks for activity recognition. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3017–3022 (2015)

    Google Scholar 

  26. Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst. Man Cybern.: Syst. PP(99), 1–10 (2017)

    Google Scholar 

  27. Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T.S., Kjærgaard, M.B., Dey, A., Sonne, T., Jensen, M.M.: Smart devices are different: assessing and mitigatingmobile sensing heterogeneities for activity recognition. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015, pp. 127–140 (2015)

    Google Scholar 

  28. Sztyler, T., Stuckenschmidt, H.: On-body localization of wearable devices: an investigation of position-aware activity recognition. In: 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–9 (2016)

    Google Scholar 

  29. Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: The mobiact dataset: recognition of activities of daily living using smartphones. In: Proceedings of the 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AgeingWell 2016, Rome, Italy, 21–22 April 2016, pp. 143–151 (2016)

    Google Scholar 

  30. Khan, A., Hammerla, N., Mellor, S., Plötz, T.: Optimising sampling rates for accelerometer-based human activity recognition. Pattern Recogn. Lett. 73, 33–40 (2016)

    Article  Google Scholar 

  31. Qi, X., Keally, M., Zhou, G., Li, Y., Ren, Z.: Adasense: adapting sampling rates for activity recognition in body sensor networks. In: 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 163–172, April 2013

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Science and Technology, TELUQ University, Montreal, Canada

    Belkacem Chikhaoui

  2. Computer Research Institute of Montreal, Montreal, Canada

    Frank Gouineau & Martin Sotir

Authors
  1. Belkacem Chikhaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Gouineau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Sotir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Belkacem Chikhaoui .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chikhaoui, B., Gouineau, F., Sotir, M. (2018). A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96133-0_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96132-3

  • Online ISBN: 978-3-319-96133-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation