Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Interactive Digital Storytelling Based Educational Games: Formalise, Author, Play, Educate and Enjoy! - The Edugames4all Project Framework Read chapter Read first chapter

2016 | OriginalPaper | Chapter

The Recognition of Human Daily Actions with Wearable Motion Sensor System

Authors : Benyue Su, Qingfeng Tang, Guangjun Wang, Min Sheng

Published in: Transactions on Edutainment XII

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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 %.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Research on Initialization of 3D Hand Pose Based on User and Computer Interaction
next chapter A Rehabilitation Planning Based on Kinect Somatosensory Recognition and Cloud Computing
Literature
1.
He, W.: Study on the key technology for human activity recognition. Doctor thesis, Chongqing University (2012)
2.
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)CrossRef
3.
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)CrossRef
4.
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)
5.
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. 12, 74–82 (2010)CrossRef
6.
Xu, C., Gu, Q., Yao, M.: Activity recognition method based on three-dimensional accelerometer. Comput. Syst. Appl. 22, 132–135 (2013)
7.
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)CrossRef
8.
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)CrossRef
9.
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)CrossRef
10.
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)CrossRef
11.
Gayathri, K.S., Elias, S.: Hierarchical activity recognition for dementia care using Markov Logic Network. J. Pers. Ubiquitous Comput. 19, 271–285 (2015)CrossRef
12.
Fahad, L.G., Khan, A., Rajarajan, M.: Activity recognition in smart homes with self verification of assignments. Neurocomputing 149, 1286–1298 (2015)CrossRef
13.
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)
14.
Lu, X., Wang, H., Wang, Y., Xu, X.: Application research on acceleration data features in human behavior recognition. Comput. Eng. 40, 178–182 (2014)
15.
Zhang, J.: Research on human behavior identification technology. Master thesis, Chongqing University (2011)
16.
MATLAB Chinese Forum: MATLAB Neural Network 30 Case Analysis. Beijing University of Aeronautics and Astronautics Press, Beijing (2010)
17.
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)
18.
Liu, G., Zhang, H., Guo, J.: The influence of different training samples to recognition system. Chin. J. Comput. 28, 1923–1928 (2005)MathSciNet
Metadata
Title
The Recognition of Human Daily Actions with Wearable Motion Sensor System
Authors
Benyue Su
Qingfeng Tang
Guangjun Wang
Min Sheng
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
Book
Transactions on Edutainment XII

Print ISBN: 978-3-662-50543-4

Electronic ISBN: 978-3-662-50544-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-662-50544-1_6

Premium Partner

    Image Credits