Skip to main content

Advertisement

SpringerLink
Log in

Semi-supervised and Unsupervised Privacy-Preserving Distributed Transfer Learning Approach in HAR Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

One of the challenges faced by machine learning in human activity recognition systems is the different distributions of the training and test samples. Transfer learning constitutes a solution to this problem. On the other hand, to perform transfer learning, it is necessary to have access to the original dataset. However, access to the dataset to implement the transfer learning algorithms results in a privacy breach. To deal with this challenge, this paper presents semi-supervised and unsupervised scenarios for privacy-preserving transfer learning in centralized and distributed manner. In the proposed distributed algorithms, it is not necessary to share the original data among the clients to implement the transfer learning algorithms. Instead, the transfer learning process can be fulfilled without having the original datasets. PPSETR and PPUSTR algorithms transfer the knowledge while preserving the privacy of the datasets on the client side. In contrast, PPDSETR and PPDUSTR algorithms provide the privacy protection of the distributed data on both the client and server sides. The proposed semi-supervised algorithms reduce the recognition error rate by 20.58% and the unsupervised algorithms decrease the recognition error rate by about 15.97% while these algorithms considerably preserve the privacy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Wang, Z., Meng, F., Yuan, G., Yan, Q., & Xia, S. (2019). An overview of human activity recognition based on smartphone. Sensing Reviews, 39(2), 288–306.

    Article  Google Scholar 

  2. Uddin, M. Z. (2019). A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. Journal of Parallel and Distributed Computing, 123, 46–53.

    Article  Google Scholar 

  3. Elbasiony, R., & Gomaa, W. (2019). A survey on human activity recognition based on temporal signals of portable inertial sensors (Vol. 921, pp. 734–745).

  4. Prati, A., Shan, C., & Wang, K. I. K. (2019). Sensors, vision and networks: From video surveillance to activity recognition and health monitoring. Journal of Ambient Intelligence and Smart Environments, 11(1), 5–22.

    Google Scholar 

  5. Ahad, M. A. R., Antar, A. D., & Ahmed, M. (2019). IoT sensor-based activity recognition (Vol. 173). Berlin: Springer.

    Google Scholar 

  6. Dang, L. M., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108, 107–561.

    Google Scholar 

  7. Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020). Deep learning for sensor-based human activity recognition: Overview. Challenges and Opportunities. arxiv:2001.07416.

  8. Morales, J., & Akopian, D. (2017). Physical activity recognition by smartphones, a survey. Biocybernetics and Biomedical Engineering, 37(3), 388–400.

    Article  Google Scholar 

  9. Fu, B., Damer, N., Kirchbuchner, F., & Kuijper, A. (2020). Sensing technology for human activity recognition: A comprehensive survey. IEEE Access, 8, 83791–83820.

    Article  Google Scholar 

  10. Bota, P., Silva, J., Folgado, D., & Gamboa, H. (2019). A semi-automatic annotation approach for human activity recognition. Sensor, 19, 501–524.

    Article  Google Scholar 

  11. Ye, J., Qi, G., Zhuang, N., Hu, H., & Hua, K. A. (2020). Learning compact features for human activity recognition via probabilistic first-take-all. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(1), 126–139.

    Article  Google Scholar 

  12. Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2015). A survey of online activity recognition using mobile phones. Sensors, 15(1), 2059–2085.

    Article  Google Scholar 

  13. Gong, Y., Fang, Y., Guo, Y., Member, S., Fang, Y., & Guo, Y. (2016). Private data analytics on biomedical sensing data via distributed computation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(3), 431–444.

    Article  Google Scholar 

  14. Cook, D., Feuz, K. D., & Krishnan, N. C. (2013). Transfer learning for activity recognition: A survey. Knowledge and Information Systems, 36(3), 537–556.

    Article  Google Scholar 

  15. Zhuang, F. et al. (2020). A comprehensive survey on transfer learning. In Proceedings of the IEEE (pp. 1–34).

  16. Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  17. Hachiya, H., Sugiyama, M., & Ueda, N. (2012). Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing, 80, 93–101.

    Article  Google Scholar 

  18. Van Kasteren, T. L. M., Englebienne, G., & Krose, B. J. A. (2008). Recognizing activities in multiple contexts using transfer learning. In AAAI fall symposium: AI in eldercare: new solutions to old problems (pp. 142–149).

  19. Zhongtang, Z., Yiqiang, C., Junfa, L., & Mingjie, L. (2010). Cross-mobile ELM based activity recognition. International Journal of Engineering and Industries, 1(1), 30–40.

    Article  Google Scholar 

  20. Zheng, V., & Hu, D. (2009). Cross-domain activity recognition. In UbiComp ’09: Proceedings of the 11th international conference on ubiquitous computing (pp. 61–70).

  21. Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2016). A survey on activity detection and classification using wearable sensors. IEEE Sensors Journal, 17(2), 386–403.

    Article  Google Scholar 

  22. Wang, Y., Cang, S., & Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137, 167–190.

    Article  Google Scholar 

  23. Garcia-Ceja, E., & Brena, R. (2015). Building personalized activity recognition models with scarce labeled data based on class similarities. In International conference on ubiquitous computing and ambient intelligence (pp. 265–276).

  24. Sarakon , S., & Tamee, K. (2020). An individual model for human activity recognition using transfer deep learning. In Joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (pp. 149–152).

  25. Abdallah, Z. S., Gaber, M. M., Srinivasan, B., & Krishnaswamy, S. (2012). StreamAR: Incremental and active learning with evolving sensory data for activity recognition. In IEEE 24th international conference on tools with artificial intelligence (pp. 1163–1170).

  26. Fallahzadeh, R., Ghasemzadeh, H. (2017). Personalization without user interruption: Boosting activity recognition in new subjects using unlabeled data. In ICCPS ’17 proceedings of the 8th international conference on cyber-physical systems (pp. 293–302).

  27. Wang, J., Chen, Y., Zheng, V. W., & Huang, M. (2018). Deep transfer learning for cross-domain activity recognition. In Proceeding of the 3rd international conference on crowd science and engineering.

  28. Hashemian, M., Razzazi, F., Zarrabi, H., & Moin, M. S. (2019). A privacy-preserving distributed transfer learning in activity recognition. Telecommunication Systems, 58(1), 1–11.

    Google Scholar 

  29. Hernandez, N., Lundström, J., Favela, J., McChesney, I., & Arnrich, B. (2020). Literature review on transfer learning for human activity recognition using mobile and wearable devices with environmental technology. SN Computer Science, 1(2), 1–16.

    Article  Google Scholar 

  30. Qiang, J., Yang, B., Li, Q., & Jing, L. (2011). Privacy-preserving SVM of horizontally partitioned data for linear classification. In Proceedings—4th international congress on image and signal processing. CISP (Vol. 5, pp. 2771–2775).

  31. Wu, T., Lin, C., & Weng, R. C. (2004). Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5, 975–1005.

    MathSciNet  MATH  Google Scholar 

  32. Zhang, X. Y., & Liu, C. L. (2013). Writer adaptation with style transfer mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1773–1787.

    Article  Google Scholar 

  33. Mangasarian, O. L., Wild, E. W., & Fung, G. M. (2007). Privacy-preserving classification of horizontally partitioned data via random kernels. Technical report, 07-03, Data Min. Institute, Computer Science Department, University of Wisconsin-Madison.

  34. Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013). A public domain dataset for human activity recognition using smartphones. In European symposium on artificial neural networks, computational intelligence and machine learning (ESANN) (pp. 24–26).

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mina Hashemian & Farbod Razzazi

  2. ICT Research Institute (Iran Telecommunication Research Center), Tehran, Iran

    Houman Zarrabi & Mohammad Shahram Moin

Authors
  1. Mina Hashemian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farbod Razzazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Houman Zarrabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Shahram Moin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farbod Razzazi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hashemian, M., Razzazi, F., Zarrabi, H. et al. Semi-supervised and Unsupervised Privacy-Preserving Distributed Transfer Learning Approach in HAR Systems. Wireless Pers Commun 117, 637–654 (2021). https://doi.org/10.1007/s11277-020-07891-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07891-1

Keywords

Search

Navigation