Abstract
This paper develops a method for recognition of human daily actions by using wearable motion sensor system. It can recognize 13 daily actions with the data in WARD1.0 efficiently. We just extract 11 features including the means and variances of vertical acceleration data of five sensors and the mean of horizontal angular speeds of the waist sensor. Then we randomly select 80 % of the samples as the training set, and the remaining samples as the test set. By removing the abnormal samples based on the confidence interval of the distance among the same type samples and using the SVM as the classifier, we present a new method for recognition of the human daily actions. Moreover, we optimize the parameters of SVM with K-CV (K-fold Cross Validation) method. The results of the experiments show that the proposed method can efficiently identify the 13 kinds of daily actions. The rate average recognition can approach to 98.5 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, W.: Study on the key technology for human activity recognition. Doctor thesis, Chongqing University (2012)
Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: A survey of online activity recognition using mobile phones. Sensors 15, 2059–2085 (2015)
Fahim, M., Fatima, I., Lee, S., Park, Y.-T.: EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer. Appl. Intell. 39, 475–488 (2013)
Yang, J., Wang, S.Q., Chen, N.J., et al.: Wearable accelerometer based extendable activity recognition system. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3641–3647 (2010)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. 12, 74–82 (2010)
Xu, C., Gu, Q., Yao, M.: Activity recognition method based on three-dimensional accelerometer. Comput. Syst. Appl. 22, 132–135 (2013)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., et al.: A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56, 871–879 (2009)
Bao, L., Intille, 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)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture recognition with a 3-D accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Morillo, L.M.S., Gonzalez-Abril, L., Ramirez, J.A.O., Concepcion, M.A.A.: Low energy physical activity recognition system on smartphones. Sensors 15, 5163–5196 (2015)
Gayathri, K.S., Elias, S.: Hierarchical activity recognition for dementia care using Markov Logic Network. J. Pers. Ubiquitous Comput. 19, 271–285 (2015)
Fahad, L.G., Khan, A., Rajarajan, M.: Activity recognition in smart homes with self verification of assignments. Neurocomputing 149, 1286–1298 (2015)
Yang, Y., Jafari, R., Sastry, S.S., Bajcsy, R.: Distributed recognition of human actions using wearable motion sensor networks. J. Ambient Intell. Smart Environ. 1, 103–115 (2009)
Lu, X., Wang, H., Wang, Y., Xu, X.: Application research on acceleration data features in human behavior recognition. Comput. Eng. 40, 178–182 (2014)
Zhang, J.: Research on human behavior identification technology. Master thesis, Chongqing University (2011)
MATLAB Chinese Forum: MATLAB Neural Network 30 Case Analysis. Beijing University of Aeronautics and Astronautics Press, Beijing (2010)
Chen, S., Li, L.: Denoising and sample reduction for large-scale sample set based on distance of nearest neighbors. Comput. Eng. 37, 184–186 (2011)
Liu, G., Zhang, H., Guo, J.: The influence of different training samples to recognition system. Chin. J. Comput. 28, 1923–1928 (2005)
Acknowledgment
The authors would like to thank the supports by the National Natural Science Foundation of China under Grant No. 11471093, the National Science and technology of China (grant No: 2014BAH13F02) and Natural Science Foundation of AQTC No.11471093 in part by the Natural Science Research Funds of Education Department of Anhui Province under Grant No. KJ2014A142.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Su, B., Tang, Q., Wang, G., Sheng, M. (2016). The Recognition of Human Daily Actions with Wearable Motion Sensor System. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XII. Lecture Notes in Computer Science(), vol 9292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-50544-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-50544-1_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-50543-4
Online ISBN: 978-3-662-50544-1
eBook Packages: Computer ScienceComputer Science (R0)