Ambient Sensors for Elderly Care and Independent Living: A Survey

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2018 Jun 27

PMID 29941804

Citations 37

Authors

Md Zia Uddin

Weria Khaksar

Jim Torresen

Affiliations

Soon will be listed here.

Abstract

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

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References

Peetoom K, Lexis M, Joore M, Dirksen C, de Witte L . Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil Rehabil Assist Technol. 2014; 10(4):271-94. DOI: 10.3109/17483107.2014.961179. View

Shahroudy A, Ng T, Yang Q, Wang G . Multimodal Multipart Learning for Action Recognition in Depth Videos. IEEE Trans Pattern Anal Mach Intell. 2015; 38(10):2123-9. DOI: 10.1109/TPAMI.2015.2505295. View

Lee T, Mihailidis A . An intelligent emergency response system: preliminary development and testing of automated fall detection. J Telemed Telecare. 2005; 11(4):194-8. DOI: 10.1258/1357633054068946. View

Celler B, Earnshaw W, Ilsar E, Harris M, Clark R, Hesketh T . Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Wales. Int J Biomed Comput. 1995; 40(2):147-55. DOI: 10.1016/0020-7101(95)01139-6. View

Perry M, Dowdall A, Lines L, Hone K . Multimodal and ubiquitous computing systems: supporting independent-living older users. IEEE Trans Inf Technol Biomed. 2004; 8(3):258-70. DOI: 10.1109/titb.2004.835533. View

Arcelus A, Herry C, Goubran R, Knoefel F, Sveistrup H, Bilodeau M . Determination of sit-to-stand transfer duration using bed and floor pressure sequences. IEEE Trans Biomed Eng. 2009; 56(10):2485-92. DOI: 10.1109/TBME.2009.2026733. View

Alwan M, Leachtenauer J, Dalal S, Kell S, Turner B, Mack D . Validation of rule-based inference of selected independent activities of daily living. Telemed J E Health. 2005; 11(5):594-9. DOI: 10.1089/tmj.2005.11.594. View

Demongeot J, Virone G, Duchene F, Benchetrit G, Herve T, Noury N . Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people. C R Biol. 2002; 325(6):673-82. DOI: 10.1016/s1631-0691(02)01480-4. View

Lee Y, Kim J, Son M, Lee M . Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network. Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:2315-8. DOI: 10.1109/IEMBS.2007.4352789. View

10.

Nyan M, Tay F, Tan A, Seah K . Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Med Eng Phys. 2006; 28(8):842-9. DOI: 10.1016/j.medengphy.2005.11.008. View

11.

Li Y, Popescu M, Ho K, Nabelek D . Improving acoustic fall recognition by adaptive signal windowing. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:7589-92. DOI: 10.1109/IEMBS.2011.6091871. View

12.

Guettari T, Aguilar P, Boudy J, Medjahed H, Istrate D, Baldinger J . Multimodal localization in the context of a medical telemonitoring system. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010:3835-8. DOI: 10.1109/IEMBS.2010.5627688. View

13.

Hein A, Winkelbach S, Martens B, Wilken O, Eichelberg M, Spehr J . Monitoring systems for the support of home care. Inform Health Soc Care. 2010; 35(3-4):157-76. DOI: 10.3109/17538157.2010.528637. View

14.

Vimarlund V, Wass S . Big data, smart homes and ambient assisted living. Yearb Med Inform. 2014; 9:143-9. PMC: 4287073. DOI: 10.15265/IY-2014-0011. View

15.

Chung K, Song K, Shin K, Sohn J, Cho S, Chang J . Noncontact Sleep Study by Multi-Modal Sensor Fusion. Sensors (Basel). 2017; 17(7). PMC: 5539697. DOI: 10.3390/s17071685. View

16.

Wang J, Liu Z, Wu Y, Yuan J . Learning Actionlet Ensemble for 3D Human Action Recognition. IEEE Trans Pattern Anal Mach Intell. 2015; 36(5):914-27. DOI: 10.1109/TPAMI.2013.198. View

17.

Tolkiehn M, Atallah L, Lo B, Yang G . Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:369-72. DOI: 10.1109/IEMBS.2011.6090120. View

18.

Leone A, Diraco G, Siciliano P . Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study. Med Eng Phys. 2011; 33(6):770-81. DOI: 10.1016/j.medengphy.2011.02.001. View

19.

Ni Q, Garcia Hernando A, Pau De La Cruz I . The Elderly's Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development. Sensors (Basel). 2015; 15(5):11312-62. PMC: 4481963. DOI: 10.3390/s150511312. View

20.

Bianchi F, Redmond S, Narayanan M, Cerutti S, Celler B, Lovell N . Falls event detection using triaxial accelerometry and barometric pressure measurement. Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2009:6111-4. DOI: 10.1109/IEMBS.2009.5334922. View