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Human Activity Recognition from Body Sensor Data using Deep Learning

  • Mobile & Wireless Health
  • Published:
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Abstract

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

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References

  1. Chen, Y., and Shen, C., Performance Analysis of smartphone-sensor behavior for human activity recognition. IEEE Access 5:3095–3110, 2017.

    Article  Google Scholar 

  2. Cornacchia, M., Ozcan, K., Zheng, Y., and Velipasalar, S., A survey on activity detection and classification using wearable sensors. IEEE Sensors 17(2):386–403, 2017.

    Article  Google Scholar 

  3. Campbell, A., and Choudhury, T., From smart to cognitive phones. IEEE Pervasive Computing 11(3):7–11, 2012.

    Article  Google Scholar 

  4. Clarkson, B.P., Life patterns: structure from wearable sensors (Ph.D. thesis), Massachusetts Institute of Technology. 2002.

  5. Avci, A., Bosch S., Marin-Perianu M., Marin-Perianu R., Havinga P., Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: International Conference on Architecture of Computing Systems, pp. 1–10. ARCS, Berlin, 2010.

  6. Lin, W., Sun, M.-T., Poovandran, R, Zhang, Z., Human activity recognition for video surveillance. In: IEEE International Symposium on Circuits and Systems, pp. 2737–2740. IEEE, Seattle, 2008.

  7. Lara, O., and Labrador, M., A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 1:1–18, 2012.

    Google Scholar 

  8. Mannini, A., and Sabatini, A. M., Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10:1154–1175, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Poppe, R., A survey on vision-based human action recognition. Image Vis. Comput. 28:976–990, 2010.

    Article  Google Scholar 

  10. Nham, B., Siangliulue, K., Yeung, S., Predicting mode of transport from iphone accelerometer data. Technical Report, Stanford University, 2008.

  11. Tapia, E., Intille, S., Larson, K., Activity recognition in the home using simple and ubiquitous sensors. In: International Conference on Pervasive Computing, pp. 158–175. Springer, Berlin, Heidelberg, 2004.

  12. Bao, L., Intille, S., Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing, pp. 1–17. Springer, Berlin, Heidelberg, 2004.

  13. Aggarwal, J., and Ryoo, M. S., Human activity analysis: a review. ACM Comput. Surv. 43(3):1–16, 2011.

    Article  Google Scholar 

  14. Tasoulis, S. K., Doukas, N., Plagianakos, V. P., and Maglogiannis, I., Statistical data mining of streaming motion data for activity and fall recognition in assistive environments. Neurocomputing 107:87–96, 2013.

    Article  Google Scholar 

  15. Behera, A., Hogg, D., Cohn, A., Egocentric activity monitoring and recovery. In: Asian Conference on Computer Vision, pp. 519–532. Springer, Berlin, Heidelberg, 2012.

  16. D. Townsend, F. Knoefel, R. Goubran, Privacy versus autonomy: a tradeoff model for smart home monitoring technologies. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4749–4752. EMBC, 2011. https://doi.org/10.1109/IEMBS.2011.6091176.

  17. Khan, A. M., Lee, Y. K., Lee, S. Y., and Kim, T. S., A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf. Technol. Biomed. 14(5):1166–1172, 2010.

    Article  PubMed  Google Scholar 

  18. Maurer, U., Smailagic, A., Siewiorek, D., and Deisher, M., Activity recognition and monitoring using multiple sensors on different body positions. In: Proc. Int. Workshop Wearable Implantable Body Sens. Netw. pp. 113–116, 2006.

  19. Kern, N., Schiele, B., Junker, H., Lukowicz, P., and Troster, G., Wearable sensing t oannotate meeting recordings. Pers. Ubiquit. Comput. 7:263–274, 2003.

    Google Scholar 

  20. Minnen, D., Starner, T., Ward, J., Lukowicz, P., and Troester, G., Recognizing and discovering human actions from on-body sensor data. In Proc. IEEE Int. Conf. Multimedia Expo. 1545–1548, 2005.

  21. Giansanti, D., Macellari, V., and Maccioni, G., New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device. Physiol. Meas. 29:11–19, 2008.

    Article  Google Scholar 

  22. Narayanan, M. R., Scalzi, M. E., Redmond, S. J., Lord, S. R., Celler, B. G., and Lovell, N. H., A wearable triaxial accelerometry system for longitudinal assessment of falls risk. In: Proc. 30th Annu. IEEE Int. Conf. Eng. Med. Biol. Soc. pp. 2840–2843, 2008.

  23. Marschollek, M., Wolf, K., Gietzelt, M., Nemitz, G., Schwabedissen, H. M. Z., and Haux, R., Assessing elderly persons’ fall risk using spectral analysis on accelerometric data—A clinical evaluation study. In: Proc. 30th Annu. IEEE Int. Conf. Eng. Med. Biol. Soc. (2008) 3682–3685.

  24. Yang, J. Y., Wang, J. S., and Chen, Y. P., Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. Pattern Recogn. Lett. 29:2213–2220, 2008.

    Article  CAS  Google Scholar 

  25. Gao, L., Bourke, A. K., and Nelson, J., A system for activity recognition using multi-sensor fusion 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7869–7872, 2011.

  26. LeCun, Y., Bengio, Y., and Hinton, G., Deep learning. Nature 521(7553):436, 2015.

    Article  PubMed  CAS  Google Scholar 

  27. Ordóñez, F. J., and Roggen, D., Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115, 2016.

    Article  PubMed Central  Google Scholar 

  28. Hammerla, N. Y., Halloran, S., and Ploetz, T., Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880, 2016

  29. Zebin, T., Scully, P. J., and Ozanyan, K. B., Human activity recognition with inertial sensors using a deep learning approach. In SENSORS, 2016 I.E. (pp. 1–3). IEEE, 2016

  30. Cheng, L., Guan, Y., Zhu, K., and Li, Y., Recognition of human activities using machine learning methods with wearable sensors. In Computing and Communication Workshop and Conference (CCWC), 2017 I.E. 7th Annual (pp. 1–7). IEEE, 2017.

  31. Ha, S., Yun, J. M., and Choi, S., Multi-modal Convolutional Neural Networks for Activity Recognition. In: 2015 I.E. International Conference on Systems, Man, and Cybernetics (SMC), 2015, pp. 3017–3022.

  32. Hassan, M. M., Uddin, M. Z., Mohamed, A., and Almogren, A., A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81:307–313, 2018.

    Article  Google Scholar 

  33. Ravi, D., Wong, C., Lo, B., and Yang, G. Z., A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE Journal of Biomedical and Health Informatics. 21(1):56–64, 2017.

    Article  PubMed  Google Scholar 

  34. Hinton, G. E., Osindero, S., and Teh, Y.-W., A fast learning algorithm for deep belief nets. Neural Comput. 18(7):1527–1554, 2006.

    Article  PubMed  Google Scholar 

  35. Uddin, M. Z., Hassan, M. M., Almogren, A., Zuair, M., Fortino, G., and Torresen, J., A facial expression recognition system using robust face features from depth videos and deep learning. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.04.019.

  36. Bulling, A., Blanke, U., and Schiele, B., A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR) 46(3):33, 2014.

    Article  Google Scholar 

  37. Ebied, H. M., Feature extraction using PCA and Kernel-PCA for face recognition. 8th International Conference on Informatics and Systems (INFOS), 72–77, 2017.

  38. Lichman, M., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.

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Acknowledgements

This paper was fully financially supported by King Saud University through the Vice Deanship of Research Chairs: Chair of Pervasive and Mobile Computing.

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Correspondence to Mohammad Mehedi Hassan.

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This article is part of the Topical Collection on Mobile & Wireless Health

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Hassan, M.M., Huda, S., Uddin, M.Z. et al. Human Activity Recognition from Body Sensor Data using Deep Learning. J Med Syst 42, 99 (2018). https://doi.org/10.1007/s10916-018-0948-z

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  • DOI: https://doi.org/10.1007/s10916-018-0948-z

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