Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2018, Article ID 9762098, 13 pages
https://doi.org/10.1155/2018/9762098
Research Article

Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition

1Department of Electrical Engineering, Kookmin University, Seoul, Republic of Korea
2Korea Institute of Industrial Technology, Ansan, Republic of Korea

Correspondence should be addressed to Gu-Min Jeong; rk.ca.nimkook@4001mg

Received 27 February 2018; Accepted 13 August 2018; Published 13 September 2018

Academic Editor: Eduard Llobet

Copyright © 2018 Nhan Duc Nguyen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. Rajesh Kanna, V. Sugumaran, T. Vijayaram, and C. Karthikeyan, “Activities of daily life (ADL) recognition using wrist-worn accelerometer,” International Journal of Engineering and Technology, vol. 8, no. 3, pp. 1406–1413, 2016. View at Google Scholar
  2. G. Koshmak, A. Loutfi, and M. Linden, “Challenges and issues in multisensor fusion approach for fall detection: review paper,” Journal of Sensors, vol. 2016, Article ID 6931789, 12 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Özdemir, “An analysis on sensor locations of the human body for wearable fall detection devices: principles and practice,” Sensors, vol. 16, no. 8, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Bao and S. S. Intille, “Activity recognition from user-annotated acceleration data,” Lecture Notes in Computer Science, vol. 3001, pp. 1–17, 2004. View at Publisher · View at Google Scholar
  5. A. M. Khan, Y.-K. Lee, S. Y. Lee, and T.-S. Kim, “A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 5, pp. 1166–1172, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. N. D. Nguyen, P. H. Truong, and G.-M. Jeong, “Daily wrist activity classification using a smart band,” Physiological Measurement, vol. 38, no. 9, pp. L10–L16, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. N. D. Nguyen, D. T. Bui, P. H. Truong, and G. M. Jeong, “Classification of five ambulatory activities regarding stair and incline walking using smart shoes,” IEEE Sensors Journal, vol. 18, no. 13, pp. 5422–5428, 2018. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Trong Bui, N. Nguyen, and G.-M. Jeong, “A robust step detection algorithm and walking distance estimation based on daily wrist activity recognition using a smart band,” Sensors, vol. 18, no. 7, 2018. View at Publisher · View at Google Scholar
  9. T. Sztyler, H. Stuckenschmidt, and W. Petrich, “Position-aware activity recognition with wearable devices,” Pervasive and Mobile Computing, vol. 38, pp. 281–295, 2017. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Zheng, “Human activity recognition based on the hierarchical feature selection and classification framework,” Journal of Electrical and Computer Engineering, vol. 2015, Article ID 140820, 9 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. M. U. S. Khan, A. Abbas, M. Ali et al., “On the correlation of sensor location and human activity recognition in body area networks (BANs),” IEEE Systems Journal, vol. 12, no. 1, pp. 82–91, 2018. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Schuldhaus, H. Leutheuser, and B. M. Eskofier, “Classification of daily life activities by decision level fusion of inertial sensor data,” in Proceedings of the 8th International Conference on Body Area Networks, pp. 77–82, Brussels, Belgium, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Baños, M. Damas, H. Pomares, and I. Rojas, “Activity recognition based on a multi-sensor meta-classifier,” in Advances in Computational Intelligence, I. Rojas, G. Joya, and J. Cabestany, Eds., pp. 208–215, Springer, Berlin, Heidelberg, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Gao, A. K. Bourke, and J. Nelson, “Activity recognition using dynamic multiple sensor fusion in body sensor networks,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1077–1080, San Diego, CA, USA, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, and Y. Amirat, “Physical human activity recognition using wearable sensors,” Sensors, vol. 15, no. 12, pp. 31314–31338, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Kunze, P. Lukowicz, H. Junker, and G. Tröster, “Where am I: recognizing on-body positions of wearable sensors,” in Location- and Context-Awareness, T. Strang and C. Linnhoff-Popien, Eds., pp. 264–275, Springer, Berlin, Heidelberg, 2005. View at Publisher · View at Google Scholar
  17. A. Vahdatpour, N. Amini, and M. Sarrafzadeh, “On-body device localization for health and medical monitoring applications,” in 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 37–44, Seattle, WA, USA, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Figueira, R. Matias, and H. Gamboa, “Body location independent activity monitoring,” in Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 190–197, Rome, Italy, 2016. View at Publisher · View at Google Scholar
  19. L. Atallah, B. Lo, R. King, and G.-Z. Yang, “Sensor positioning for activity recognition using wearable accelerometers,” IEEE Transactions on Biomedical Circuits and Systems, vol. 5, no. 4, pp. 320–329, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Zhang and A. A. Sawchuk, “A feature selection-based framework for human activity recognition using wearable multimodal sensors,” in Proceedings of the 6th International ICST Conference on Body Area Networks, vol. 6, pp. 92–98, Beijing, China, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Pirttikangas, K. Fujinami, and T. Nakajima, “Feature selection and activity recognition from wearable sensors,” in Ubiquitous Computing Systems, pp. 516–527, Springer, Berlin, Heidelberg, 2006. View at Publisher · View at Google Scholar
  22. T. Huynh and B. Schiele, “Analyzing features for activity recognition,” in Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence Innovative Context-Aware Services: Usages and Technologies—sOc-EUSAI ‘05, pp. 159–163, Grenoble, France, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Y. Yang, R. Jafari, S. S. Sastry, and R. Bajcsy, “Distributed recognition of human actions using wearable motion sensor networks,” Journal of Ambient Intelligence and Smart Environments, vol. 1, no. 2, pp. 103–115, 2009. View at Google Scholar
  24. O. Banos, M. Damas, H. Pomares, A. Prieto, and I. Rojas, “Daily living activity recognition based on statistical feature quality group selection,” Expert Systems with Applications, vol. 39, no. 9, pp. 8013–8021, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. O. Banos, M. Damas, H. Pomares, F. Rojas, B. Delgado-Marquez, and O. Valenzuela, “Human activity recognition based on a sensor weighting hierarchical classifier,” Soft Computing, vol. 17, no. 2, pp. 333–343, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Gao, A. K. Bourke, and J. Nelson, “Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems,” Medical Engineering & Physics, vol. 36, no. 6, pp. 779–785, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Li, K. Cheng, S. Wang et al., “Feature selection: a data perspective,” ACM Computing Surveys, vol. 50, no. 6, pp. 1–45, 2018. View at Publisher · View at Google Scholar · View at Scopus
  28. K. Kira and L. A. Rendell, “A practical approach to feature selection,” in Machine Learning Proceedings 1992, Morgan Kaufmann Publishers Inc., 1992. View at Publisher · View at Google Scholar
  29. R. Gilad-Bachrach, A. Navot, and N. Tishby, “Margin based feature selection-theory and algorithms,” in ICML ‘04 Proceedings of the Twenty-First International Conference on Machine Learning, vol. 21, pp. 43–50, Banff, Alberta, Canada, July 2004. View at Publisher · View at Google Scholar
  30. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  32. A. W. Whitney, “A direct method of nonparametric measurement selection,” IEEE Transactions on Computers, vol. C-20, no. 9, pp. 1100–1103, 1971. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Leutheuser, D. Schuldhaus, and B. M. Eskofier, “Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset,” PLoS One, vol. 8, no. 10, article e75196, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. E. Zdravevski, P. Lameski, V. Trajkovik et al., “Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering,” IEEE Access, vol. 5, pp. 5262–5280, 2017. View at Publisher · View at Google Scholar · View at Scopus
  35. L. Yu and H. Liu, “Feature selection for high-dimensional data: a fast correlation-based filter solution,” in Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 856–863, Washington, DC, USA, 2003.
  36. Y. Saeys, I. Inza, and P. Larranaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. I. Kononenko, E. Simec, and M. Robnik-Sikonja, “Overcoming the myopia of inductive learning algorithms with relief,” Applied Intelligence, vol. 7, no. 1, pp. 39–55, 1997. View at Publisher · View at Google Scholar · View at Scopus
  38. X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” Advances in Neural Information Processing Systems, vol. 18, 2006. View at Google Scholar
  39. F. R. K. Chung, “Spectral graph theory,” in Regional Conference Series in Mathematics, vol. 92, American Mathematical Society, 1996. View at Publisher · View at Google Scholar
  40. M. Kudo and J. Sklansky, “Comparison of algorithms that select features for pattern classifiers,” Pattern Recognition, vol. 33, no. 1, pp. 25–41, 2000. View at Publisher · View at Google Scholar · View at Scopus
  41. Z. Zhao, L. Wang, H. Liu, and J. Ye, “On similarity preserving feature selection,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 3, pp. 619–632, 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. A. M. De Silva and P. H. W. Leong, Grammar-Based Feature Generation for Time-Series Prediction, Springer, 2015.
  43. J.-Y. Yang, J.-S. Wang, and Y.-P. Chen, “Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers,” Pattern Recognition Letters, vol. 29, no. 16, pp. 2213–2220, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at Scopus
  45. P. Cunningham and S. J. Delaney, “-nearest neighbor classifiers,” Technical Report UCD-CSI-2007-4, School of Computer Science and Informatics, Ireland, 2007. View at Google Scholar
  46. H. Bhaskar, D. C. Hoyle, and S. Singh, “Machine learning in bioinformatics: a brief survey and recommendations for practitioners,” Computers in Biology and Medicine, vol. 36, no. 10, pp. 1104–1125, 2006. View at Publisher · View at Google Scholar · View at Scopus