Advertisement

Human Activity Dataset Generation

  • Jorge Luis Reyes OrtizEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

This chapter presents the collection of stages required for the acquisition of the experimental HAR data used in this thesis. It includes aspects such as smart phone selection, trials with volunteers, signal conditioning and feature selection. It also describes the procedures concerning the dataset validation; internally through experimentation and externally via a HAR contest in which other research groups were encouraged to propose novel solutions to the recognition problem.

Keywords

Inertial Sensor Window Sample Learn Vector Quantization Human Activity Recognition Body Acceleration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. C.B. Abdelkader, R. Cutler, L. Davis, in Stride and Cadence as a Biometric in Automatic Person Identification and Verification, International Conference on Automatic Face and Gesture Recognition, 2002Google Scholar
  2. Android (2013) Android developers. http://developer.android.com/index.html. Accessed 05 Nov 2013
  3. M. Aupetit, Nearly homogeneous multi-partitioning with a deterministic generator. Neuro Comput 72, 1379–1389 (2009)Google Scholar
  4. K. Bache, M. Lichman, UCI machine learning repository (2013) http://archive.ics.uci.edu/ml
  5. L. Bao, S.S. Intille, Activity recognition from user-annotated acceleration data. Pervasive. Comput. (2004)Google Scholar
  6. B. Bruno, F. Mastrogiovanni, A. Sgorbissa, T. Vernazza, R. Zaccaria. in Human Motion Modelling and Recognition: A Computational Approach, IEEE International Conference on Automation Science and Engineering, 2012Google Scholar
  7. C.C. Chang, C.J. Lin, LIBSVM: A Libraries for support vector machine. ACM Trans. Intell. Syst. Technol. 2, 27–54 (2011)CrossRefGoogle Scholar
  8. S. Dernbach, B. Das, N.C. Krishnan, B.L. Thomas, D.J. Cook, in Simple and Complex Activity Recognition Through Smart Phones, International Conference on Intelligent Environments, 2012Google Scholar
  9. P. Duhamel, M. Vetterli, Fast fourier transforms: a tutorial review and a state of the art. Sig. proce. 19, 259–299 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  10. A. Gupta, C. Milanesi, R. Cozza, C.K. Lu, Market share analysis: mobile phones, worldwide, 2q13. Technical report, Gartner Inc. (2013)Google Scholar
  11. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11, 10–18 (2009)Google Scholar
  12. J.J.C. Ho. Interruptions: using activity transitions to trigger proactive messages. Ph.D. thesis, Massachusetts Institute of Technology, 2004Google Scholar
  13. M.D. Karantonis, M.R. Narayanan, M. Mathie, N.H. Lovell, B.G. Celler, Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10, 156–167 (2006)CrossRefGoogle Scholar
  14. M. Kästner, M. Strickert, T. Villmann, in A Sparse Kernelized Matrix Learning Vector Quantization Model for Human Activity Recognition, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013Google Scholar
  15. A.M. Khan, Y.K Lee, S.Y Lee, T.S Kim, in Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis, IEEE International Conference on Future Information Technology, 2010Google Scholar
  16. K.V. Laerhoven, O. Cakmakci, in What Shall We Teach Our Pants International Symposium on Wearable Computers, 2000Google Scholar
  17. A. Reiss, G. Hendeby, D. Stricker, inA Competitive Approach for Human Activity Recognition on Smartphones, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013Google Scholar
  18. J.L. Reyes-Ortiz, D. Anguita, A. Ghio, L. Oneto, X. Parra. Human activity recognition using smartphones data set (2013), http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
  19. J.L. Reyes-Ortiz, D. Anguita, A. Ghio, L. Oneto, X. Parra. Recognition of basic activities and postural transitions using smartphones data set (2014), http://www.har.smartlab.ws
  20. D. Rodríguez-Martín. Sistema inercial vestible amb capacitat de desenvolupament implementació algorísmica. Master’s thesis, Universitat Politècnica de Catalunya, 2010Google Scholar
  21. D. Rodríguez-Martín, C. Pérez-López, A. Samà, J. Cabestany, A. Català, A wearable inertial measurement unit for long-term monitoring in the dependency care area. Sens 13, 14079–14104, (2013a)Google Scholar
  22. D. Rodríguez-Martín, A. Samà, C. Perez-Lopez, A. Català, J. Cabestany, A. Rodriguez-Molinero, Svm-based posture identification with a single waist-located triaxial accelerometer. Expert. Syst. Appl. 40, 7203–7211 (2013b)Google Scholar
  23. D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha, in Collecting Complex Activity Data Sets in Highly Rich Networked Sensor Environments, International Conference on Networked Sensing Systems, 2010Google Scholar
  24. B. Romera-Paredes, H. Aung, N. Bianchi-Berthouze, inA One-vs-One Classifier Ensemble With Majority Voting for Activity Recognition, European Symposium Artificial Neural Networks, Computational Intelligence Machine Learn, 2013Google Scholar
  25. K. Roth, I. Kauppinen, P.A.A. Esquef, V. Valimaki, in Frequency Warped Burg’s Method For Ar-Modeling, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2003Google Scholar
  26. A. Samà, Human movement analysis by means of accelerometers: application to human gait and motor symptoms of Parkinsons disease. Ph.D. thesis, Universitat Politécnica de Catalunya (2013)Google Scholar
  27. A. Samà, C. Angulo, D. Pardo, A. Català, J. Cabestany, Analyzing human gait and posture by combining feature selection and kernel methods. Neuro Comput 74, 2665–2674 (2011)Google Scholar
  28. E. Tapia, S. Intille, L. Lopez, K. Larson, in The Design of A Portable Kit of Wireless Sensors for Naturalistic Data Collection, Pervasive Computing, 2006Google Scholar
  29. J. Wang, Q. Chen, Y. Chen, in Rbf Kernel Based Support Vector Machine With Universal Approximation and its Application, Advances in Neural Networks—ISNN, 2004Google Scholar
  30. J.Y. Yang, J.S. Wang, Y.P. Chen, Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recogn. Lett. 29, 2213–2220 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CETpDUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations