Abstract
In recent years, smartphone-based human activity recognition has become a promising research field of mobile computing, and is widely applied in inertial positioning, fall detection, and personalized recommendation. In practical scenario, smartphone can be placed at several body positions, such as trouser pocket, jacket pocket and so on. Since data is collected from the accelerometer embedded in smartphone, different body locations cannot generate consistent data for the same activity. As a result, the samples at a new position usually obtains low recognition rate from the classifier trained by the original data collected from other positions. In this paper, we propose a COntinuity-based POsition-adaptive recognition method, abbreviated COPO, for dealing with this problem. Considering the continuous results with high probability of correct recognition, we select them as the retraining data in COPO for updating the initial classifier. To prove the effectiveness of retraining data selecting method theoretically, we use Hidden Markov Model (HMM) to calculate the probability that the continuous recognition results are correctly recognized. Finally, a number of experiments are designed to verify our COPO, including data collection, performance comparison, and parameter analysis. The results show that the recognition rate of COPO is 2.62 % higher than other common methods.
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Notes
- 1.
When the activity duration is 2, the continuous number threshold is set to 2.
References
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. (CSUR) 46(3), 33 (2014)
Chen, Y., Zhao, Z., Wang, S., Chen, Z.: Extreme learning machine-based device displacement free activity recognition model. Soft Comput. 16(9), 1617–1625 (2012)
Deng, W.Y., Zheng, Q.H., Wang, Z.M.: Cross-person activity recognition using reduced kernel extreme learning machine. Neural Netw. 53, 1–7 (2014)
Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 838–845. IEEE (2005)
Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)
Hoseini-Tabatabaei, S.A., Gluhak, A., Tafazolli, R.: A survey on smartphone-based systems for opportunistic user context recognition. ACM Comput. Surv. (CSUR) 45(3), 27 (2013)
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)
Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1997)
Khan, S.S., Karg, M.E., Hoey, J., Kulic, D.: Towards the detection of unusual temporal events during activities using HMMs. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1075–1084. ACM (2012)
Lin, S.J., Chang, C., Hsu, M.F.: Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowl.-Based Syst. 39, 214–223 (2013)
Mazilu, S., Blanke, U., Dorfman, M., Gazit, E., Mirelman, A., Hausdorff, J.M., Tröster, G.: A wearable assistant for gait training for parkinsons disease with freezing of gait in out-of-the-lab environments. ACM Trans. Interact. Intell. Syst. (TiiS) 5(1), 5 (2015)
Nguyen, N.T., Phung, D.Q., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 955–960. IEEE (2005)
Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zee: zero-effort crowdsourcing for indoor localization. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, pp. 293–304. ACM (2012)
Wang, C., Zhang, J., Li, M., Yuan, Y., Xu, Y.: A smartphone location independent activity recognition method based on the angle feature. In: Sun, X., et al. (eds.) ICA3PP 2014. LNCS, vol. 8630, pp. 179–191. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11197-1_14
Wang, C., Zhang, J., Wang, Z., Wang, J.: Position-independent activity recognition model for smartphone based on frequency domain algorithm. In: 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT), pp. 396–399. IEEE (2013)
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., Campbell, A.T.: Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–14. ACM (2014)
Acknowledgments
This work was supported by The Natural Science Foundation of Tianjin(No. 16JCQNJC00700).
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Wang, C., Xu, Y., Zhang, J., Yu, W. (2016). COPO: A Novel Position-Adaptive Method for Smartphone-Based Human Activity Recognition. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_1
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DOI: https://doi.org/10.1007/978-3-319-49178-3_1
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