A wearable fall detector for elderly people

Mušálek, Miroslav

dc.title	A wearable fall detector for elderly people	en
dc.contributor.author	Mušálek, Miroslav
dc.relation.ispartof	Annals of DAAAM and Proceedings of the International DAAAM Symposium
dc.identifier.issn	1726-9679 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-3-902734-11-2
dc.date.issued	2017
dc.citation.spage	1015
dc.citation.epage	1020
dc.event.title	28th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2017
dc.event.location	Zadar
utb.event.state-en	Croatia
utb.event.state-cs	Chorvatsko
dc.event.sdate	2017-11-08
dc.event.edate	2017-11-11
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	Danube Adria Association for Automation and Manufacturing, DAAAM
dc.identifier.doi	10.2507/28th.daaam.proceedings.141
dc.relation.uri	http://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2017/141.pdf
dc.subject	Accelerometer	en
dc.subject	Fall detection	en
dc.subject	Gyroscope	en
dc.subject	Wearable	en
dc.description.abstract	Falls and their subsequent possible side effects are for elderly people and people with various illnesses, such as epilepsy, are present the main obstacle to an independent and more dignified life. Even for people living independently, falls are common occurrences. Proper use of a fall detector will not only add more security to the seniors, but will also allow for timely medical intervention that can be life-critical. One of the limitations of this wearable technology is its limited lifetime and the occurrence of possible errors during the data acquisition and processing process. A fall detector designed to be able to monitor the activity of elderly people and designed to make the necessary warnings to ensure their safety as a result of the fall. In addition, other options that are used for this purpose are compared. This paper summarizes the functionality, architecture and implementation of the system.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007746
utb.identifier.obdid	43877924
utb.identifier.scopus	2-s2.0-85040737889
utb.source	d-scopus
dc.date.accessioned	2018-02-26T10:20:05Z
dc.date.available	2018-02-26T10:20:05Z
dc.description.sponsorship	ERDF, European Regional Development Fund
dc.rights	Attribution-NonCommercial 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.rights.access	openAccess
utb.contributor.internalauthor	Mušálek, Miroslav
utb.fulltext.affiliation	Miroslav Musalek Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, Zlin 760 05, Czech Republic
utb.fulltext.dates	-
utb.fulltext.references	[1] Topinkova, E. (2005). Geriatrics for practice, Prague, ISBN 80-7262-365-6 [2] Gibson, M.J. et al. (1987). "The prevention of falls in later life. And report of the Kellogg International Work Group on the Prevention of Falls by the Elderly "Danish Medical Bulletin, Supplement 34 4:1-24, 1987. [3] Brownsell, S. a M. S. Hawley (2004). Automatic fall detectors and the fear of falling. Journal of Telemedicine and Telecare of. 10 (5): 262-266 DOI: 10.1258/1357633042026251. ISSN 1357-633x. [4] Pannurat, Natthapon, Surapa Thiemjarus a Ekawit Nantajeewarawat (2014). Automatic Fall Monitoring: A Review. Sensors. 14(7): 12900-12936. DOI:10.3390/s140712900. ISSN 1424-8220. [5] Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Lyon, France, 22-26 August 2007. 2007. Piscataway, NJ: IEEE Service Center [distributor], 16 v. (cx, 6759, 13, 46 p.). ISBN 14-244-0787-7. [6] Miaou, S.-G., Pei-hsu Sung a Chia-Yuan Huang (2006). A Customized Human Fall Detection System Using OmniCamera Images and Personal Information. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2. IEEE, s. 39-42. DOI: 10.1109/DDHH.2006.1624792. ISBN 1-4244-0058-9. [7] Shane Kim, Jihoon Ryoo (2017). "WiSDom: A model-driven solitary death prevention system based on WiFi signals and real-time supervised training", Consumer Communications & Networking Conference (CCNC) 2017 14th IEEE Annual, pp. 131-136, 2017, ISSN 2331-9860 [8] Albert MV, Kording K, Herrmann M, Jayaraman A (2012). Fall Classification by Machine Learning Using Mobile Phones. PLoS ONE 7 (5): e36556. https://doi.org/10.1371/journal.pone.0036556 [9] Igual, Raul, Carlos Medrano and Inmaculada Plaza (2013). Challenges, issues and trends in fall detection systems. BioMedical Engineering OnLine. 12(1): DOI: 10.1186/1475-925X-12-66. ISSN 1475-925 x. [10] Sriborrirux, W.; Leamsumran, P.; Dan-klang,P. (2014). Real-time system for monitoring activity among the elderly using an RF SoC device with triaxial accelerometer data over a wireless sensor network:2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications(IMWSBio), Thailand,December 8-10,2014 [C].IEEE,2014. [11] Yanjun Li,Gan Chen,Yueyun Shen,et al. (2012). Accelerometer- based fall detection sensor system for the elderly:2012 IEEE 2nd International Conference on Cloud Computer and Intelligent Systems (CCIS),Hangzhou, China, October 30- November 1,2012[C].IEEE,2012. [12] Aguiar,B.;Rocha,T.;Silva,J., et al. (2014). Accelerometer- based fall detection for smartphones: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), June 11-12, 2014 [C].IEEE,2014. [13] Nyan, M., et al. (2006). Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer. in Journal of Physics: Conference Series. 2006. IOP Publishing. [14] Li, Q., et al. (2009). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. in Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on. 2009. IEEE. [15] Kuncar, A[les] (2016). Basic Techniques for Filtering Noise Out of Accelerometer Data, Proceedings of the 26th DAAAM International Symposium, pp.1122-1128, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-07-5, ISSN 1726-9679, Vienna, Austria DOI: 10.2507/26th.daaam.proceedings.158 [16] Quoc T. Huynh, Uyen D. Nguyen, Lucia B. Irazabal, Nazanin Ghassemian, and Binh Q. Tran (2015). “Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm,” Journal of Sensors, vol. 2015, Article ID 452078, 8 pages, 2015. doi:10.1155/2015/452078
utb.fulltext.sponsorship	This work was supported by the European Regional Development Fund under the project CEBIA-Tech Instrumentation No. CZ.1.05/2.1.00/19.0376 and also by the Internal Grant Agency of Tomas Bata University under the project No. IGA/FAI/2017/015.
utb.scopus.affiliation	Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, Czech Republic
utb.fulltext.projects	CZ.1.05/2.1.00/19.0376
utb.fulltext.projects	IGA/FAI/2017/015