Abstract
In the field of sensor based human activity recognition, fixed sampling rate scheme is difficult to accommodate the dynamic characteristics of streaming data. It may directly leads to high energy consumption or activities detail missing problems. In this paper, an efficiency online activity recognition framework is proposed by integrating sampling rate optimization with novel class detection and recurring class detection algorithms. Based on the proposed framework, we believe that this system can effectively save battery life and computation capacity without decreasing the overall recognition performance.
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Notes
- 1.
b is a novel class if it never appears in stream data, while c is a recurring class if it appeared before but disappeared so long that c is discarded from current ensemble.
- 2.
Ensemble learning: A sort of machine learning algorithm by combining several weak learner to construct a strong learner.
References
Al-Khateeb, T., et al.: Recurring and novel class detection using class-based ensemble for evolving data stream. IEEE Trans. Knowl. Data Eng. 28(10), 2752–2764 (2016)
Deng, C., Yuan, W., Tao, Z., Cao, J.: Detecting novel class for sensor-based activity recognition using reject rule. In: Li, W., et al. (eds.) IDCS 2016. LNCS, vol. 9864, pp. 34–44. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45940-0_4
Khan, A., Hammerla, N.Y., Mellor, S., Plötz, T.: Optimising sampling rates for accelerometer-based human activity recognition. Pattern Recognit. Lett. 73, 33–40 (2016)
Qi, X., Keally, M., Zhou, G., Li, Y., Ren, Z.: AdaSense: adapting sampling rates for activity recognition in body sensor networks. In: 19th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2013, Philadelphia, PA, USA, 9–11 April 2013, pp. 163–172 (2013)
Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)
Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44(1), 103–115 (2015)
Acknowledgment
The work was partially supported by National Natural Science Foundation of China under the Grant No. 61502360, No. 61571336 and No. 61503291.
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Jin, Z., Cao, J., Sun, J., Li, W., Wang, Q. (2018). An Online Adaptive Sampling Rate Learning Framework for Sensor-Based Human Activity Recognition. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J. (eds) Internet and Distributed Computing Systems. IDCS 2018. Lecture Notes in Computer Science(), vol 11226. Springer, Cham. https://doi.org/10.1007/978-3-030-02738-4_24
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DOI: https://doi.org/10.1007/978-3-030-02738-4_24
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