Human Activity Recognition from Body Sensor Data using Deep Learning
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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.
KeywordsHuman activity recognition Body sensor data Deep learning Deep belief network
This paper was fully financially supported by King Saud University through the Vice Deanship of Research Chairs: Chair of Pervasive and Mobile Computing.
- 4.Clarkson, B.P., Life patterns: structure from wearable sensors (Ph.D. thesis), Massachusetts Institute of Technology. 2002.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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
- 10.Nham, B., Siangliulue, K., Yeung, S., Predicting mode of transport from iphone accelerometer data. Technical Report, Stanford University, 2008.Google Scholar
- 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.Google Scholar
- 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.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.Google Scholar
- 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.
- 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.Google Scholar
- 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.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.Google Scholar
- 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.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.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, 2016Google Scholar
- 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, 2016Google Scholar
- 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.Google Scholar
- 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.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.
- 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.Google Scholar
- 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.