Soft Computing

, Volume 21, Issue 17, pp 5053–5060 | Cite as

Determining the optimal number of body-worn sensors for human activity recognition

Methodologies and Application

Abstract

Recent developments in sensors increased the importance of action recognition. Generally, the previous studies were based on the assumption that the complex actions can be recognized by more features. Therefore, generally more than required body-worn sensor types and sensor nodes were used by the researchers. On the other hand, this assumption leads many drawbacks, such as computational complexity, storage and communication requirements. The main aim of this paper is to investigate the applicability of recognizing the actions without degrading the accuracy with less number of sensors by using a more sophisticated feature extraction and classification method. Since, human activities are complex and include variable temporal information in nature, in this study one-dimensional local binary pattern, which is sensitive to local changes, and the grey relational analysis, which can successfully classify incomplete or insufficient datasets, were employed for feature extraction and classification purposes, respectively. Achieved mean classification accuracies by the proposed approach are 95.69, 98.88, and 99.08 % while utilizing all data, data obtained from a sensor node attached to left calf and data obtained from only 3D gyro sensors, respectively. Furthermore, the results of this study showed that the accuracy obtained by using only a 3D acceleration sensor attached in the left calf, 98.8 %, is higher than accuracy obtained by using all sensor nodes, 95.69 %, and reported accuracies in the previous studies that made use of the same dataset. This result highlighted that the position and type of sensors are much more important than the number of utilized sensors.

Keywords

Sensor reduction Activity recognition Local binary patterns Grey relational analysis Wearable sensors 

References

  1. Aggarwal JK, Xia L (2014) Human activity recognition from 3D data: a review. Pattern Recognit Lett 48:70–80. doi:10.1016/j.patrec.2014.04.011 CrossRefGoogle Scholar
  2. Altun K, Barshan B, Tunçel O (2010) Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit 43:3605–3620. doi:10.1016/j.patcog.2010.04.019 CrossRefMATHGoogle Scholar
  3. Bache K, Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml
  4. Banos O, Toth MA, Damas M, Pomares H, Rojas I (2014) Dealing with the effects of sensor displacement in wearable activity recognition. Sensors 14(6):9995–10023. doi:10.3390/s140609995 CrossRefGoogle Scholar
  5. Banos O, Galvez JM, Damas M, Pomares H, Rojas I (2014) Window size impact in activity recognition. Sensors 14(4):6474–6499. doi:10.3390/s140406474 CrossRefGoogle Scholar
  6. Banos O, Damas M, Pomares H, Rojas I (2013) Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the international work conference on neural networks (IWANN 2013), TenerifeGoogle Scholar
  7. Banos O, Damas M, Pomares H, Rojas I (2013) Handling displacement effects in on-body sensor-based activity recognition. In: Ambient assisted living and active aging, pp 80–87. doi:10.1007/978-3-319-03092-0_12
  8. Banos O, Toth MA, Damas M, Pomares H, Rojas I, Amft O (2012) A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the 14th international conference on ubiquitous computing (Ubicomp 2012), PittsburghGoogle Scholar
  9. Chaquet JM, Carmona EJ, Fernández-Caballero A (2013) A survey of video datasets for human action and activity recognition. Comput Vis Image Underst 117:633–659. doi:10.1016/j.cviu.2013.01.013 CrossRefGoogle Scholar
  10. Chavarriaga R, Sagha H, Calatroni A, Digumarti ST, Tröster G, Millán JR, Roggen D (2013) The opportunity challenge: a benchmark database for on-body sensor-based activity recognition. Pattern Recognit Lett 34:2033–2042. doi:10.1016/j.patrec.2012.12.014 CrossRefGoogle Scholar
  11. Chernbumroong S, Cang S, Yu H (2014) A practical multi-sensor activity recognition system for home-based care. Decis Support Syst 66:61–70. doi:10.1016/j.dss.2014.06.005 CrossRefGoogle Scholar
  12. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1:131–156. doi:10.1016/S1088-467X(97)00008-5 CrossRefGoogle Scholar
  13. Deng JL (1982) Control problems of grey system. Syst Control Lett 1:288–294. doi:10.1016/S0167-6911(82)80025-X MathSciNetCrossRefMATHGoogle Scholar
  14. Deng JL (1989) Grey information space. J Grey Syst 1:103–117MathSciNetMATHGoogle Scholar
  15. Fortino G, Galzarano S, Gravina R, Li W (2014) A framework for collaborative computing and multi-sensor data fusion in body sensor Networks. Inf Fusion 22:50–70. doi:10.1016/j.inffus.2014.03.005 CrossRefGoogle Scholar
  16. Gao L, Bourke AK, Nelson J (2014) Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med Eng Phys 36:779–785. doi:10.1016/j.medengphy.2014.02.012 CrossRefGoogle Scholar
  17. Gu T, Chen S, Tao X, Lu J (2010) An unsupervised approach to activity recognition and segmentation based on object-use fingerprints. Data Knowl Eng 69:533–544. doi:10.1016/j.datak.2010.01.004 CrossRefGoogle Scholar
  18. Guiry JJ, Ven P, Nelson J, Warmerdam L, Riper H (2014) Activity recognition with smartphone support. Med Eng Phys 36:670–675. doi:10.1016/j.medengphy.2014.02.009 CrossRefGoogle Scholar
  19. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATHGoogle Scholar
  20. Hachaj T, Ogiela MR, Koptyra K (2015) Application of assistive computer vision methods to oyama karate techniques recognition. Symmetry 7(4):1670–1698. doi:10.1016/j.dsp.2015.07.004
  21. Hachaj T, Ogiela MR (2014) Rule-based approach to recognizing human body poses and gestures in real time. Multimed Syst 20(1):81–99. doi:10.1007/s00530-013-0332-2
  22. Hachaj T, Ogiela MR (2015) Full body movements recognition–unsupervised learning approach with heuristic R-GDL method. Dig Signal Process 46:239–252. doi:10.1016/j.dsp.2015.07.004
  23. Kaya Y, Uyar M, Tekin R, Yıldırım S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209–219. doi:10.1016/j.amc.2014.05.128
  24. Kaya Y (2015) Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis. Australas Phys Eng Sci Med 38(3):435–446CrossRefGoogle Scholar
  25. Kwon Y, Kang K, Bae C (2014) Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst Appl 41:6067–6074. doi:10.1016/j.eswa.2014.04.037 CrossRefGoogle Scholar
  26. Ladha L, Deepa T (2011) Feature selection methods and algorithms. Int J Comput Sci Eng (IJCSE) 3(5):1787–1797Google Scholar
  27. Lin Y, Lee H, Chang PC (2009) Practical expert diagnosis model based on the grey relational analysis technique. Expert Syst Appl 36(2):1523–1528. doi:10.1016/j.eswa.2007.11.046 CrossRefGoogle Scholar
  28. Lin Y, Liu S (2004) A historical introduction to grey systems theory. Proc IEEE Int Conf Syst Man Cybern 1:2403–2408Google Scholar
  29. Okeyo G, Chen L, Wang H, Sterritt R (2014) Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mobile Comput 10:155–172. doi:10.1016/j.pmcj.2012.11.004 CrossRefGoogle Scholar
  30. Pediaditis M, Tsiknakis M, Leitgeb N (2012) Vision-based motion detection, analysis and recognition of epileptic seizures—a systematic review. Comput Methods Programs Biomed 108:1133–1148. doi:10.1016/j.cmpb.2012.08.005 CrossRefGoogle Scholar
  31. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(6):2297–2301. doi:10.1073/pnas.88.6.2297 MathSciNetCrossRefMATHGoogle Scholar
  32. Punchoojit L, Hongwarittorrn N (2015) A comparative study on sensor displacement effect on realistic sensor displacement benchmark dataset. In: Recent advances in information and communication technology, pp 97–106. doi:10.1007/978-3-319-19024-2_10
  33. Renyi A (1961) On measures of entropy and information. In: Fourth Berkeley symposium on mathematical statistics and probability, pp 547–561Google Scholar
  34. Reyes-Ortiz JL, Oneto L, Samà A, Parra X, Anguita D (2016) Transition-aware human activity recognition using smartphones. Neurocomputing 171:754–767. doi:10.1016/j.neucom.2015.07.085 CrossRefGoogle Scholar
  35. Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL (2012) Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications. Maturitas 71:13–19. doi:10.1016/j.maturitas.2011.11.003 CrossRefGoogle Scholar
  36. Wang L, Hu W, Tan T (2003) Recent developments in human motion analysis. Pattern Recognit 36:585–601. doi:10.1016/S0031-3203(02)00100-0 CrossRefGoogle Scholar
  37. Wang L, Gu T, Tao X, Lu J (2012) A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive Mobile Comput 8:115–130. doi:10.1016/j.pmcj.2010.12.001 CrossRefGoogle Scholar
  38. Wilson J, Najjar N, Hare J, Gupta S (2015) Human activity recognition using LZW-coded probabilistic finite state automata. In: IEEE international conference on robotics and automation (ICRA), pp 3018–3023. doi:10.1109/ICRA.2015.7139613
  39. Ye J, Stevenson G, Dobson S (2014) KCAR: a knowledge-driven approach for concurrent activity recognition. Pervasive Mobile Comput. doi:10.1016/j.pmcj.2014.02.003 (in Press)
  40. Yin J, Tian G, Feng Z, Li J (2014) Human activity recognition based on multiple order temporal information. Comput Electr Eng 40:1538–1551. doi:10.1016/j.compeleceng.2014.04.006 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringBatmanTurkey
  2. 2.Department of Computer EngineeringSiirt UniversitySiirtTurkey

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