Elderly activities recognition and classification for applications in assisted living

Expert Systems with Applications

Volumn 40, Issue 5, 2013, Pages 1662-1674

  Chernbumroong, Saisakul ^a,b   Cang, Shuang ^c   Atkins, Anthony ^a   Yu, Hongnian ^c  

^a STAFFORDSHIRE UNIVERSITY   (United Kingdom)

^b CHIANG MAI UNIVERSITY   (Thailand)

^c BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

Activities of Daily Livings (ADLs); Assisted living systems; Elderly care; Feature selection and classification; Wrist worn multi sensors

Indexed keywords

ACTIVITIES OF DAILY LIVING; ASSISTED LIVING; ELDERLY CARE; FEATURE SELECTION AND CLASSIFICATION; MULTI SENSOR;

BIOLOGICAL SYSTEMS; STATISTICAL TESTS;

SENSORS;

EID: 84872029933     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.09.004     Document Type: Article

References (37)

1. E. Aarts Ambient intelligence: A multimedia perspective IEEE Multimedia 11 2004 12 19 ID: 1
2. 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 39 2012 8013 8021
3. Bayer, A., Harper, L., Greenwald, M., & of Retired Persons A. A. (2000). Fixing to stay: A national survey of housing and home modification issues. American Association of Retired Persons.
4. J.A. Botia, A. Villa, and J. Palma Ambient assisted living system for in-home monitoring of healthy independent elders Expert Systems with Applications 39 2012 8136 8148
5. A.A. Chaaraoui, P. Climent-Prez, and F. Flrez-Revuelta A review on vision techniques applied to human behaviour analysis for ambient-Assisted living Expert Systems with Applications 39 2012 10873 10888
6. C.-C. Chang, and C.-J. Lin Libsvm: A library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 2011 27:1 27:27
7. S. Chernbumroong, A. Atkins, and H. Yu Activity classification using a single wrist-worn accelerometer Fifth international conference on software, knowledge information, industrial management and applications (SKIMA), 2011 2011 IEEE 1 6
8. Chernbumroong, S., Atkins, A. S., & Yu, H. (2010). Perception of smart home technologies to assist elderly people. In The fourth international conference on software, knowledge, information management and applications (SKIMA 2010) (pp. 90-97).
9. A. Costa, J.C. Castillo, P. Novais, A. Fernndez-Caballero, and R. Simoes Sensor-driven agenda for intelligent home care of the elderly Expert Systems with Applications 39 2012 12192 12204
10. G. Demiris, B.K. Hensel, M. Skubic, and M. Rantz Senior residents' perceived need of and preferences for "smart home" sensor technologies International Journal of Technology Assessment in Health Care 24 2008 120 124 (Pubitemid 351158551)
11. G. Demiris, M. Rantz, M. Aud, K. Marek, H. Tyrer, and M. Skubic Older adults' attitudes towards and perceptions of'smart home'technologies: A pilot study Informatics for Health and Social Care 29 2004 87 94 (Pubitemid 39232661)
12. 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 2010 274 283 ID: 1
13. D. Fuentes, L. Gonzalez-Abril, C. Angulo, and J. Ortega Online motion recognition using an accelerometer in a mobile device Expert Systems with Applications 39 2012 2461 2465
14. I. Guyon, and A. Elisseeff An introduction to variable and feature selection The Journal of Machine Learning Research 3 2003 1157 1182
15. N. Györbíró, A. Fábián, and G. Hományi An activity recognition system for mobile phones Mobile Networks and Applications 14 2009 82 91
16. X. Hong, C. Nugent, M. Mulvenna, S. McClean, B. Scotney, and S. Devlin Evidential fusion of sensor data for activity recognition in smart homes Pervasive and Mobile Computing 5 2009 236 252
17. 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 2010 446 455
18. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification. Technical report. Department of Computer Science, National Taiwan University.
19. C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13 2002 415 425
20. P.-C. Huang, S.-S. Lee, Y.-H. Kuo, and K.-R. Lee A flexible sequence alignment approach on pattern mining and matching for human activity recognition Expert Systems with Applications 37 2010 298 306
21. T. Kleinberger, M. Becker, E. Ras, A. Holzinger, and P. Muller Ambient intelligence in assisted living: enable elderly people to handle future interfaces C. Stephanidis, Universal access in human-computer interaction: Ambient interaction UAHCI'07 2007 Springer-Verlag Berlin, Heidelberg 103 112
22. O. Kwon, J.M. Shim, and G. Lim Single activity sensor-based ensemble analysis for health monitoring of solitary elderly people Expert Systems with Applications 39 2012 5774 5783
23. F. Mahoney, and D. Barthel Functional evaluation: The barthel index Maryland State Medical Journal 14 1965 56 61
24. F. Martínez-Pérez, J. González-Fraga, J. Cuevas-Tello, and M. Rodríguez Activity inference for ambient intelligence through handling artifacts in a healthcare environment Sensors 12 2012 1072 1099
25. 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
26. B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C.J. Bula, and P. Robert Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly IEEE Transactions on Biomedical Engineering 50 2003 711 723 ID: 1 (Pubitemid 36723605)
27. H.G. Nguyen, A. Bouzerdoum, and S.L. Phung Learning pattern classification tasks with imbalanced data sets Pattern Recognition 2009 193 208
28. D.N. Olivieri, I.G. Conde, and X.A.V. Sobrino Eigenspace-based fall detection and activity recognition from motion templates and machine learning Expert Systems with Applications 39 2012 5935 5945
29. 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 2006 119 128 (Pubitemid 43092325)
30. Poh, M. (2011). Continuous assessment of epileptic seizures with wrist-worn biosensors. Ph.D. Thesis. Massachusetts Institute of Technology.
31. M.E. Pollack, L. Brown, D. Colbry, C.E. McCarthy, C. Orosz, and B. Peintner Autominder: An intelligent cognitive orthotic system for people with memory impairment Robotics and Autonomous Systems 44 2003 273 282
32. Texas Instruments, T.I. (2010). eZ430-Chronos development tool user's guide. Manual SLAU292C.
33. United Nations, D. o. E., & Social Affairs, P. D. (2011). World population prospects: The 2010 revision, cd-rom edition.
34. W. Wang, P. Jones, and D. Partridge Assessing the impact of input features in a feedforward neural network Neural Computing and Applications 9 2000 101 112
35. J. Yin, Q. Yang, and J.J. Pan Sensor-based abnormal human-Activity detection IEEE Transactions on Knowledge and Data Engineering 20 2008 1082 1090
36. H. Yu, M. Spenko, and S. Dubowsky An adaptive shared control system for an intelligent mobility aid for the elderly Autonomous Robots 15 2003 53 66
37. C. Zhu, and W. Sheng Motion- and location-based online human daily activity recognition Pervasive Mobile Computing 7 2011 256 269