Abstract
With the rapid development of smartphone, human activity recognition based on acceleration sensors attracts much attention in the academic and industry recently. However, the recognition accuracy is not ideal due to the diversity of human activities and other environmental factors. A real-time user activities monitoring system is developed on android, and comparison of several feature extraction and classification algorithms is carried out. Based on the monitoring system, a feature called (TF4+FFT10) is proposed. Experiment result shows that the recognition accuracy rate of feature (TF4+FFT10) with the adopted KNN algorithm is 98.6 %.
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References
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35395-6_30
Xue, Y.: Human activity recognition based on single acceleration sensor. South China University of Technology (2011)
Li, D.: Design of a human daily activity recognition system based on three-axis acceleration sensor. Instrum. Technol. (2013)
Mathie, M.J., Coster, A.C.F., Lovell, N.H., et al.: Accelerometry: providing anintegrated, practical method for long-term, ambulatory monitoring of human movement. Physiol. Meas. 25(2), 1–20 (2005)
Kern, N., Antifakos, S., Schiele, B., et al.: A model of human interruptability: experimental evaluation and automatic estimation from wearable sensors. In: Proceedings of ISWC, Washington DC, USA, pp. 158–165 (2004)
Karantonis, D.M., Narayanan, M.R., et al.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006). A Publication of the IEEE Engineering in Medicine and Biology Society
He, Z., Jin, L.: Activity recognition from acceleration data using AR model representation and SVM. In: IEEE International Conference on Machine Learning and Cybernetics, pp. 2245–2250 (2008)
Khan A.M., Lee Y.-K., Lee S.Y., et al.: Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: International Conference on Future Information Technology, pp. 1–6 (2010)
Jennifer, R.K., Gary, M.W., Samuel, A.M.: Activity recognition using cell phone accelerometers. In: SensorKDD 2010, 25 July, Washington, DC, USA, 9 pp (2010)
Li, N.: Research on wearable health monitoring system based on human activity recognition. Beijing University of Technology (2013)
Qiu, M., Zhong, M., Li, K., Gai, K., Zong, Z.: Phase-change memory optimization for green cloud with genetic algorithm. IEEE Trans. Comput. 64(12), 3528–3540 (2015)
Gai, K., Qiu, M., Zhao, H.: Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing. IEEE Trans. Cloud Comput. (2016)
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This work is supported by the National Natural Science Foundation of China under Grants NSFC 61672358.
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Cai, S., Shan, Z., Zeng, T., Yin, J., Ming, Z. (2017). Human Activity Recognition Based on Smart Phone’s 3-Axis Acceleration Sensor. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_17
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DOI: https://doi.org/10.1007/978-3-319-52015-5_17
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