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Koshmak, G., Loutfi, A. & Lindén, M. (2016). Challenges and Issues in Multi-Sensor FusionApproach for Fall Detection: Review Paper. Journal of Sensors, Article ID 6931789.
Open this publication in new window or tab >>Challenges and Issues in Multi-Sensor FusionApproach for Fall Detection: Review Paper
2016 (English)In: Journal of Sensors, ISSN 1687-725X, E-ISSN 1687-7268, article id 6931789Article in journal (Refereed) Published
Abstract [en]

Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

Keywords
Aging societies; Body movements; Emergency situation; Fall detection; Multifusion; Review papers; Sensor technologies; Single sensor
National Category
Health Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-27955 (URN)10.1155/2016/6931789 (DOI)000368282900001 ()2-s2.0-84953297157 (Scopus ID)
Available from: 2015-05-10 Created: 2015-05-10 Last updated: 2017-12-04Bibliographically approved
Koshmak, G., Lindén, M. & Loutfi, A. (2016). Fall risk probability estimation based on supervised feature learning using public fall datasets. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2016: . Paper presented at 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016; Disney's Contemporary ResortOrlando; United States; 16 August 2016 through 20 August 2016; (pp. 752-755). , Article ID 7590811.
Open this publication in new window or tab >>Fall risk probability estimation based on supervised feature learning using public fall datasets
2016 (English)In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2016, 2016, p. 752-755, article id 7590811Conference paper, Published paper (Refereed)
Abstract [en]

Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-34725 (URN)10.1109/EMBC.2016.7590811 (DOI)2-s2.0-85009115435 (Scopus ID)9781457702204 (ISBN)
Conference
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016; Disney's Contemporary ResortOrlando; United States; 16 August 2016 through 20 August 2016;
Available from: 2017-01-26 Created: 2017-01-26 Last updated: 2017-01-26Bibliographically approved
Åkerberg, A., Koshmak, G., Johansson, A. & Lindén, M. (2015). Heart rate measurement as a tool to quantify sedentary behavior. In: Studies in Health Technology and Informatics, vol. 211: . Paper presented at 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2015, 2 June 2015 through 4 June 2015 (pp. 105-110).
Open this publication in new window or tab >>Heart rate measurement as a tool to quantify sedentary behavior
2015 (English)In: Studies in Health Technology and Informatics, vol. 211, 2015, p. 105-110Conference paper, Published paper (Refereed)
Abstract [en]

Sedentary work is very common today. The aim of this pilot study was to attempt to differentiate between typical work situations and to investigate the possibility to break sedentary behavior, based on physiological measurement among office workers. Ten test persons used one heart rate based activity monitor (Linkura), one pulse oximeter device (Wrist) and one movement based activity wristband (Fitbit Flex), in different working situations. The results showed that both heart rate devices, Linkura and Wrist, were able to detect differences in heart rate between the different working situations (resting, sitting, standing, slow walk and medium fast walk). The movement based device, Fitbit Flex, was only able to separate differences in steps between slow walk and medium fast walk. It can be concluded that heart rate measurement is a promising tool for quantifying and separating different working situations, such as sitting, standing and walking. 

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-28788 (URN)10.3233/978-1-61499-516-6-105 (DOI)2-s2.0-84939237142 (Scopus ID)9781614995159 (ISBN)
Conference
12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2015, 2 June 2015 through 4 June 2015
Available from: 2015-08-28 Created: 2015-08-28 Last updated: 2015-08-28Bibliographically approved
Koshmak, G. (2015). Remote Monitoring and Automatic Fall Detection for Elderly People at Home. (Licentiate dissertation). Västerås: Mälardalen University
Open this publication in new window or tab >>Remote Monitoring and Automatic Fall Detection for Elderly People at Home
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Aging population is a one of the key problems for the vast majority of so called "more economically developed countries" (MEDC). The amount of elderly people who suffer from multiple disease and require permanent monitoring of their vital parameters has increased recently resulting in extra healthcare costs. Modern healthcare systems exploited in geriatric medicine are often obtrusive and require patients presence at the hospital which interferes with their demand in independent life style. Recent developments on telecare market provide a wide range of wireless solutions for distant monitoring of medical parameters and health assistance. However, most of the devices are programmed for spot checking and operate independently from each other. There is still a lack of integrated framework with high interoperability and on-line continuous monitoring support for further correlation analyses. The current study is a step towards complete and continuous data collection system for elderly people with various types of health problems. Research initiative is motivated by recent demand in reliable multi-functional remote monitoring systems, combining different data sources. The main focus is made on fall detection methods, interoperability, real-life testing and correlation analyses. The list of main contributions contains (1) investigating communication functionalities, (2) developing algorithm for reliable fall detection, (3) multi-sensor fusion analyses and overview of the latest multi-sensor fusion approaches, (4) user study involving healthy volunteers and elderly people. Evaluation is performed through a series of computer simulation and real-life testing in collaboration with the local medical authorities. As a result we expect to obtain a monitoring system with reliable communication capabilities, inbuilt on-line processing, alarm generating techniques and complete functionality for integration with similar systems or smart-home environment.

Abstract [sv]

En åldrande befolkning utgör ett av de viktigaste problemen för de allra flesta så kallade "mer ekonomiskt utvecklade länder" (MEDC). Mängden äldre människor som lider av multi-sjukdomar och kräver ständig övervakning av vitala parametrar har ökat på senare tid, vilket resulterar i ökade sjukvårdskostnader. Geriatrikens moderna sjukvårdssystem kräver ofta att patienterna är närvarande på sjukhuset, vilket kraftigt begränsar en självständig och oberoende livsstil. Den senaste utvecklingen på telemedicinområdet erbjuder ett brett utbud av trådlösa lösningar inom hälsovård för distansövervakning av medicinska parametrar. De flesta lösningarna innebär punktkontroll av enskilda parametrar och arbetar oberoende av varandra. Det saknas fortfarande integrerade lösningar med hög interoperabilitet och kontinuerlig on-line övervakningsstöd för att kunna genomföra ytterligare korrelationsanalyser. Detta arbete utgör ett steg mot ett fullständigt och kontinuerligt datainsamlingssystem för äldre personer med olika typer av hälsoproblem. Forskningsinitiativet motiveras av senaste tidens efterfrågan på tillförlitliga multifunktionella system för distansövervakning, som kombinerar olika datakällor. Huvudfokus utgörs av falldetektionsmetoder, interoperabilitet, verkliga tester och korrelationsanalyser. Listan över de främsta bidragen innehåller (1) att undersöka kommunikationsfunktionaliteter, (2) utveckla en algoritm för tillförlitlig falldetektion, (3) multisensor-fusion-analyser och översikt över multisensor-fusion-strategier, (4) en användarstudie med friska frivilliga äldre. Utvärderingen sker genom en serie av datorsimuleringar och tester i verklig miljö i samarbete med lokala hälso- och sjukvårdsmyndigheter. Målet är ett övervakningssystem med tillförlitliga kommunikationsmöjligheter, inbyggd on-line-bearbetning, tekniker för larmgenerering och funktionalitet för integration med liknande system eller i en smart hemmiljö.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 207
Keywords
remote monitoring, aging population, fall detection
National Category
Medical and Health Sciences Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-27945 (URN)978-91-7485-212-7 (ISBN)
Presentation
2015-06-15, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2015-05-05 Created: 2015-05-05 Last updated: 2018-01-11Bibliographically approved
Koshmak, G., Lindén, M. & Loutfi, A. (2014). Dynamic Bayesian networks for context-aware fall risk assessment. Sensors (Switzerland), 14(5), 9330-9348
Open this publication in new window or tab >>Dynamic Bayesian networks for context-aware fall risk assessment
2014 (English)In: Sensors (Switzerland), ISSN 1424-8220, Vol. 14, no 5, p. 9330-9348Article in journal (Refereed) Published
Abstract [en]

Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.

Keywords
Ambient assisted living (AAL), Context recognition, Dynamic Bayesian networks (DBN), Fall detection, Multi-sensor fusion
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-25229 (URN)10.3390/s140509330 (DOI)000337112200090 ()2-s2.0-84901335545 (Scopus ID)
Available from: 2014-06-13 Created: 2014-06-13 Last updated: 2018-02-23Bibliographically approved
Koshmak, G., Lindén, M. & Loutfi, A. (2013). Evaluation of the android-based fall detection system with physiological data monitoring. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: . Paper presented at 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, 3 July 2013 through 7 July 2013, Osaka (pp. 1164-1168).
Open this publication in new window or tab >>Evaluation of the android-based fall detection system with physiological data monitoring
2013 (English)In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, p. 1164-1168Conference paper, Published paper (Refereed)
Abstract [en]

Aging population is considered to be major problem in modern healthcare. At the same time, fall incidents often occur among elderly and cause serious injuries affecting their independent living.

Series
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, ISSN 1557-170X
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-22818 (URN)10.1109/EMBC.2013.6609713 (DOI)000341702101163 ()2-s2.0-84886522255 (Scopus ID)9781457702167 (ISBN)
Conference
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, 3 July 2013 through 7 July 2013, Osaka
Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2018-02-23Bibliographically approved
Koshmak, G., Ekström, M. & Lindén, M. (2012). A smart-phone based monitoring system with health device profile. In: : . Paper presented at Medicinteknikdagarna 2012, 2-3 oktober, Lund.
Open this publication in new window or tab >>A smart-phone based monitoring system with health device profile
2012 (English)Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-17462 (URN)
Conference
Medicinteknikdagarna 2012, 2-3 oktober, Lund
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2018-03-05Bibliographically approved
Koshmak, G., Ekström, M. & Lindén, M. (2012). A smart-Phone Based Monitoring System with Health Device Profile for Measuring Vital Physiological parameters. In: : . Paper presented at World Congress on Medical Physics and Biomedical Engineering, Beijing, China, May 26-31, 2012.
Open this publication in new window or tab >>A smart-Phone Based Monitoring System with Health Device Profile for Measuring Vital Physiological parameters
2012 (English)Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-17463 (URN)
Conference
World Congress on Medical Physics and Biomedical Engineering, Beijing, China, May 26-31, 2012
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2018-03-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0712-6015

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