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Optimal model selection for posture recognition in home-based healthcare

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Abstract

This paper investigates optimal model selection for posture recognition. Accuracy and computational time are related to the trained model in a supervised classification. An optimal model selection is important for a reliable activity monitoring system. Conventional guidance on model training uses large instances of randomly selected data in order to characterize the classes. A new approach to the training of a multiclass support vector machine (SVM) model suited to limited training sets such as used in posture recognition is provided. This approach picks a small training set from misclassified data to improve an initial model in an iterative and incremental fashion. In addition, a two step grid-search algorithm is used for the parameters setting. The best parameters were chosen according to the testing accuracy rather than conventional validating accuracy. This new approach for model selection was evaluated against conventional approaches in an activity classification study. Nine everyday postures were classified from a belt-worn smart phone’s accelerometer data. The classification derived from the small training set and the conventional randomly selected training set differed in two aspects: classification performance to new data (85.1% Pick-out small training set vs. 70.3% conventional large training set) and computational efficiency (improved 28%).

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References

  1. Abe S (2005) Support vector machines for pattern classification. Springer-Verlag, New York

    Google Scholar 

  2. Abe S, Inoue T (2001) Fast training of support vector machines by extracting boundary data. Artif Neural Netw ICANN 2130:308–313

    Google Scholar 

  3. Allen FR, Ambikairajah E, Lovell NH, Celler BG (2006) Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiol Meas 27(10):935–952

    Article  Google Scholar 

  4. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Pervasive Comput 3001:1–17

    Google Scholar 

  5. Boulay B, Brémond F, Thonnat M (2006) Applying 3D human model in a posture recognition system. Pattern Recogn Lett 27(15):1788–1796

    Article  Google Scholar 

  6. Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7:456–461

    Article  Google Scholar 

  7. Bussmann J, Martens W, Tulen J, Schasfoort F, VAN DEN BE (2001) Measuring daily behavior using ambulatory accelerometry: the activity monitor. Behav Res Methods Instrum Comput 33(3):349

    Article  Google Scholar 

  8. Bremner D, Demaine E, Erickson J, Iacono J, Langerman S, Morin P, Toussaint G (2005) Output-sensitive algorithms for computing nearest-neighbor decision boundaries. Discrete Comput Geom 33(4):593–604

    Article  MathSciNet  MATH  Google Scholar 

  9. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines, 2001) Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

  10. Cucchiara R, Grana C, Prati A, Vezzani R (2005) Probabilistic posture classification for human-behavior analysis. IEEE Trans Syst Man Cybern Part A 35(1):42–54

    Article  Google Scholar 

  11. Domingos P, Pazzani M (1997) On the the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2–3):103–130

    Article  MATH  Google Scholar 

  12. Fletcher R (1981) Practical methods of optimization: constrained optimization. vol 2. John Wiley & Sons, Inc., Somerset, NJ, p 224

  13. Foerster F, Smeja M, Fahrenberg J (1999) Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Comput Hum Behav 15(5):571–583

    Article  Google Scholar 

  14. Hall M, Frank E (2008) Combining naive Bayes and decision tables, In Proc 21st Florida Artificial Intelligence Research Society Conference, Miami, Florida. AAAI Press

  15. Harms H, Amft O, Tröster G, Roggen D (2008) Smash: a distributed sensing and processing garment for the classification of upper body postures. Proceedings of the ICST 3rd international conference on body area networks ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 22

  16. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Article  Google Scholar 

  17. Juang CF, Chang CM (2007) Human body posture classification by a neural fuzzy network and home care system application. IEEE Trans Syst Man Cybern Part A 37(6):984–994

    Article  MathSciNet  Google Scholar 

  18. Karhu O, Kansi P, Kuorinka I (1977) Correcting working postures in industry: a practical method for analysis. Appl Ergon 8(4):199–201

    Article  Google Scholar 

  19. Knerr S, Personnaz L, Dreyfus G, Fogelman J, Agresti A, Ajiz M, Jennings A, Alizadeh F, Alizadeh F, Haeberly J (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. Optim Methods Softw 1:23–34

    Google Scholar 

  20. Koggalage R, Halgamuge S (2004) Reducing the number of training samples for fast support vector machine classification. Neural Inf Process Lett Rev 2(3):57–65

    Google Scholar 

  21. Lee S, Park H, Hong S, Lee K, Kim Y (2003) A study on the activity classification using a triaxial accelerometer. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE

  22. Martiskainen P, Järvinen M, Skön JP, Tiirikainen J, Kolehmainen M, Mononen J (2009) Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 119(1–2):32–38

    Article  Google Scholar 

  23. Mathie MJ, Celler BG, Lovell NH, Coster ACF (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42(5):679–687

    Article  Google Scholar 

  24. Mattmann C, Clemens F, Tröster G (2008) Sensor for measuring strain in textile. Sensors 8(6):3719

    Article  Google Scholar 

  25. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: rapid prototyping for complex data mining tasks. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 935–940

  26. Panini L, Cucchiara R (2003) A machine learning approach for human posture detection in domotics applications. Image Analysis and Processing, 2003. Proceedings. 12th International Conference on, pp 103

  27. Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999. pp 1541

  28. Sohn S, Dagli CH (2002) Advantages of using fuzzy class memberships in self-organizing map and support vector machines. Neural Networks, 2001. Proceedings. IJCNN’01. International Joint Conference on IEEE, pp 1886

  29. Sung M, Marci C, Pentland A (2005) Wearable feedback systems for rehabilitation. J Neuroeng Rehabil 2:17

    Article  Google Scholar 

  30. Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24(6):774–780

    Google Scholar 

  31. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. The Morgan Kaufmann series in data management system, Elsevier

  32. Yang T (2006) Computational verb decision trees. Int J Comput Cogn 4(4):34–46

    Google Scholar 

  33. Zhang S, McCullagh P, Nugent C, Zheng H (2009) A theoretic algorithm for fall and motionless detection. Pervasive computing technologies for healthcare, Proceedings of the 3rd Annual IEEE International Conference, pp 1–6

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Acknowledgments

The authors acknowledge the support of University of Ulster Vice Chancellor Scholarship Programme, and thank all members of the Smart Environments Research Group for their help with collecting the experimental data. Special thanks are due to Mr W Burns for assisting with the code for data collection using the HTC phone.

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Authors and Affiliations

  1. Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, BT37 0QB, Northern Ireland, UK

    Shumei Zhang, Paul McCullagh, Chris Nugent, Huiru Zheng & Matthias Baumgarten

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  1. Shumei Zhang
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  2. Paul McCullagh
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  3. Chris Nugent
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  4. Huiru Zheng
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  5. Matthias Baumgarten
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Correspondence to Paul McCullagh.

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Zhang, S., McCullagh, P., Nugent, C. et al. Optimal model selection for posture recognition in home-based healthcare. Int. J. Mach. Learn. & Cyber. 2, 1–14 (2011). https://doi.org/10.1007/s13042-010-0009-5

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  • DOI: https://doi.org/10.1007/s13042-010-0009-5

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