A practical multi-sensor activity recognition system for home-based care

Decision Support Systems

Volumn 66, Issue , 2014, Pages 61-70

  Chernbumroong, Saisakul ^a   Cang, Shuang ^a   Yu, Hongnian ^a  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

Classification; Feature selection; Home based care; Multi sensor activity recognition; Mutual information

Indexed keywords

FEATURE EXTRACTION;

ACTIVITY RECOGNITION; HOME-BASED; MULTI SENSOR; MUTUAL INFORMATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84908220736     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2014.06.005     Document Type: Article

References (22)

1. UN World population prospects: the 2012 revision URL: http://esa.un.org/unpd/wpp/index.htm 2012
2. J.M. Corchado, J. Bajo, Y. de Paz, and D.I. Tapia Intelligent environment for monitoring Alzheimer patients, agent technology for health care Decision Support Systems 44 2 2008 382 396
3. Z. Yang, A. Kankanhalli, B.Y. Ng, and J.T.Y. Lim Analyzing the enabling factors for the organizational decision to adopt healthcare information systems Decision Support Systems 55 3 2013 764 776
4. C. Combes, and J. Azema Clustering using principal component analysis applied to autonomy-disability of elderly people Decision Support Systems 55 2 2013 578 586
5. U. Maurer, A. Rowe, A. Smailagic, and D. Siewiorek Location and activity recognition using eWatch: a wearable sensor platform Ambient Intelligence in Everyday Life 2006 86 102
6. S. Chernbumroong, S. Cang, A. Atkins, and H. Yu Elderly activities recognition and classification for applications in assisted living Expert Systems with Applications 40 5 2013 1662 1674
7. A. Fleury, M. Vacher, and N. Noury SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results IEEE Transactions on Information Technology in Biomedicine 14 2 2010 274 283 (ID: 1)
8. Y.J. Hong, I.J. Kim, S.C. Ahn, and H.G. Kim Mobile health monitoring system based on activity recognition using accelerometer Simulation Modelling Practice and Theory 18 4 2010 446 455
9. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen Activity classification using realistic data from wearable sensors IEEE Transactions on Information Technology in Biomedicine 10 1 2006 119 128
10. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou, and Y. Amirat An unsupervised approach for automatic activity recognition based on hidden Markov model regression IEEE Transactions on Automation Science and Engineering 10 3 2013 829 835
11. Z. Wang, M. Jiang, Y. Hu, and H. Li An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors IEEE Transactions on Information Technology in Biomedicine 16 4 2012 691 699
12. Y. Nam, and J. Park Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor IEEE Journal of Biomedical and Health Informatics 17 2 2013 420 426
13. H. Gjoreski, and M. Gams Activity/posture recognition using wearable sensors placed on different body locations Proceeding of Signal and Image Processing and Applications 2011 22 24
14. C. Lombriser, N.B. Bharatula, D. Roggen, and G. Tröster On-body activity recognition in a dynamic sensor network Proceedings of the ICST 2nd international conference on Body area networks; BodyNets '07 17 2007 1 17 (6)
15. E. Munguia Tapia Using Machine Learning for Real-Time Activity Recognition and Estimation of Energy Expenditure (Ph.D. thesis) 2008 Massachusetts Institute of Technology
16. 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 27 2005 1226 1238
17. P. Estevez, M. Tesmer, C. Perez, and J. Zurada Normalized mutual information feature selection IEEE Transactions on Neural Networks 20 2 2009 189 201
18. S. Cang, and H. Yu Mutual information based input feature selection for classification problems Decision Support Systems 54 1 2012 691 698
19. W. Wang, P. Jones, and D. Partridge Assessing the impact of input features in a feedforward neural network Neural Computing & Applications 9 2 2000 101 112
20. C.E. Shannon A mathematical theory of communication SIGMOBILE: Mobile Computing and Communications Review 5 1 2001 3 55
21. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press, Inc. New York, NY, USA
22. C.C. Chang, and C.J. Lin LIBSVM: a library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 27 2011 1 27 (27)