Abstract
Segmenting sensor events for activity recognition has many key challenges due to its unsupervised nature, the real-time requirements necessary for on-line event detection, and the possibility of having to recognise overlapping activities. A further challenge is to achieve robustness of classification due to sub-optimal choice of window size. In this paper, we present a novel real-time recognition framework to address these problems. The proposed framework is divided into two phases: off-line modeling and on-line recognition. In the off-line phase a representation called Activity Features (AFs) are built from statistical information about the activities from annotated sensory data and a Naïve Bayesian (NB) classifier is modeled accordingly. In the on-line phase, a dynamic multi-feature windowing approach using AFs and the learnt NB classifier is introduced to segment unlabeled sensor data as well as predicting the related activity. How this on-line segmentation occurs, even in the presence of overlapping activities, diverges from many other studies. Experimental results demonstrate that our framework can outperform the state-of-the-art windowing-based approaches for activity recognition involving datasets acquired from multiple residents in smart home test-beds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The mean is equal to \(S_T/N\). The standard deviation is equal to \(\sqrt{(SS_T/N)-(S_T/N)^2}\).
References
Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Adaptive mobile activity recognition system with evolving data streams. Neurocomputing 150, 304–317 (2015)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24646-6_1
Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27(1), 32–38 (2012)
Cook, D.J., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods Inf. Med. 48(5), 480–485 (2009)
Gu, T., Wu, Z., Tao, X., Pung, H.K., Lu, J.: epsicar: an emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In: IEEE International Conference on Pervasive Computing and Communications (PerCom 2009), pp. 1–9. IEEE (2009)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Huỳnh, T., Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75160-1_4
Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate Discrete Distributions, vol. 444. Wiley, Hoboken (2005)
Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138–154 (2014)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Nguyen, H.M., Cooper, E.W., Kamei, K.: Online learning from imbalanced data streams. In: 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 347–352. IEEE (2011)
Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014)
Peterek, T., Penhaker, M., Gajdoš, P., Dohnálek, P.: Comparison of classification algorithms for physical activity recognition. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol. 237, pp. 123–131. Springer, Cham (2014). doi:10.1007/978-3-319-01781-5_12
Preece, S.J., Goulermas, J.Y., Kenney, L.P., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensorsa review of classification techniques. Physiol. Meas. 30(4), R1–R33 (2009)
Shahi, A., Woodford, B.J., Deng, J.D.: Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In: Pfahringer, B., Renz, J. (eds.) AI 2015. LNCS, vol. 9457, pp. 505–516. Springer, Cham (2015). doi:10.1007/978-3-319-26350-2_45
Sobhani, P., Viktor, H., Matwin, S.: Learning from imbalanced data using ensemble methods and cluster-based undersampling. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2014. LNCS, vol. 8983, pp. 69–83. Springer, Cham (2015). doi:10.1007/978-3-319-17876-9_5
Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM (2008)
Wan, J., O’Grady, M.J., O’Hare, G.M.: Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Personal Ubiquitous Comput. 19(2), 287–301 (2015)
Wiss, S., Kulikowsk, C.: Computer systems that learn: classification and prediction methods from statistics. In: Neural Networks, Machine Learning and Expert Systems. Morgan Kaufmann, San Mateo (1991)
Yala, N., Fergani, B., Fleury, A.: Feature extraction for human activity recognition on streaming data. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), pp. 1–6. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shahi, A., Woodford, B.J., Lin, H. (2017). Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-67274-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67273-1
Online ISBN: 978-3-319-67274-8
eBook Packages: Computer ScienceComputer Science (R0)