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Elderly activities recognition and classification for applications in assisted living

Published on Apr 1, 2013in European Journal of Combinatorics0.91
· DOI :10.1016/j.eswa.2012.09.004
Saisakul Chernbumroong6
Estimated H-index: 6
(Staffordshire University),
Shuang Cang15
Estimated H-index: 15
(BU: Bournemouth University)
+ 1 AuthorsHongnian Yu24
Estimated H-index: 24
(BU: Bournemouth University)
Abstract
Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low-powered consumption sensors are now available which can be used in assisted living applications to provide sensitive and responsive services based on users current environments and situations. This paper aims to address the issue of how to develop an activity recognition method for a practical assisted living system in term of user acceptance, privacy (non-visual) and cost. The paper proposes an activity recognition and classification method for detection of Activities of Daily Livings (ADLs) of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors. Experimental results demonstrate that the proposed method can achieve a high classification rate (>90%). Statistical tests are employed to support this high classification rate of the proposed method. Also, we prove that by combining data from temperature sensor and/or altimeter with accelerometer, classification accuracy can be improved.
  • References (35)
  • Citations (158)
References35
Newest
#1íNgelo Costa (University of Minho)H-Index: 1
#2José Carlos Castillo Montoya (UCLM: University of Castilla–La Mancha)H-Index: 14
Last.Ricardo Simoes (Polytechnic Institute of Cávado and Ave)H-Index: 17
view all 5 authors...
#1Oresti Banos (UGR: University of Granada)H-Index: 16
#2Miguel Damas (UGR: University of Granada)H-Index: 15
Last.Ignacio Rojas (UGR: University of Granada)H-Index: 30
view all 5 authors...
#1Ohbyung Kwon (Kyung Hee University)H-Index: 17
#2Jae Moon Shim (Kyung Hee University)H-Index: 1
Last.Geunchan Lim (South Korean Ministry for Health, Welfare and Family Affairs)H-Index: 1
view all 3 authors...
#1Daniel Fuentes (University of Seville)H-Index: 20
#2Luis Gonzalez-Abril (University of Seville)H-Index: 14
Last.Juan Antonio Ortega (University of Seville)H-Index: 26
view all 4 authors...
#1Francisco E. Martinez-Perez (UABC: Autonomous University of Baja California)H-Index: 4
#2Jose A. González-Fraga (Universidad Autónoma de San Luis Potosí)H-Index: 5
Last.Marcela D. Rodríguez (ACM: Association for Computing Machinery)H-Index: 17
view all 4 authors...
#1Saisakul Chernbumroong (CMU: Chiang Mai University)H-Index: 6
#2Anthony Atkins (Staffordshire University)H-Index: 8
Last.Hongnian Yu (Staffordshire University)H-Index: 24
view all 3 authors...
#1Chun Zhu (OSU: Oklahoma State University–Stillwater)H-Index: 12
#2Weihua Sheng (OSU: Oklahoma State University–Stillwater)H-Index: 26
Cited By158
Newest
#1Dario Ortega Anderez (NTU: Nottingham Trent University)H-Index: 2
#2Ahmad Lotfi (NTU: Nottingham Trent University)H-Index: 14
Last.Amir Pourabdollah (NTU: Nottingham Trent University)H-Index: 6
view all 3 authors...
#1Yan Wang (BU: Bournemouth University)H-Index: 2
#2Shuang Cang (Yanshan University)H-Index: 1
Last.Hongnian Yu (BU: Bournemouth University)H-Index: 24
view all 3 authors...
#1Salisu Wada Yahaya (NTU: Nottingham Trent University)H-Index: 2
#2Ahmad Lotfi (NTU: Nottingham Trent University)H-Index: 14
Last.Mufti Mahmud (NTU: Nottingham Trent University)H-Index: 13
view all 3 authors...
#1Dalai Tang (Inner Mongolia University of Finance and Economics)
#2Naoyuki Kubota (Tokyo Metropolitan University)H-Index: 22
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