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Multimodal sensor-based fall detection within the domestic environment of elderly people

Multimodale sensorengestützte Sturzerkennung im häuslichen Bereich älterer Menschen

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Zeitschrift für Gerontologie und Geriatrie Aims and scope Submit manuscript

Abstract

Background

Falls represent a major threat to the health of the elderly and are a growing burden on the healthcare systems. With the growth of the elderly population within most societies efficient fall detection becomes increasingly important; however, existing fall detection systems still fail to produce reliable results.

Objectives

A study was carried out on sensor-based fall detection, analysis of falls with the help of fall protocols and the analysis of user acceptance of fall detection sensor technology through questionnaires.

Material and methods

A total of 28 senior citizens were recruited from a German community-dwelling population. The primary goal was a sensor-based detection of falls with accelerometers, video cameras and microphones. Details of the falls were analyzed with the help of medical geriatric assessments and standardized fall protocols. The study duration was 8 weeks and required a maximum of nine visits per subject.

Results

The study participants were 28 subjects with a mean age of 74.3 and a standard deviation (SD) of ± 6.3 years of which 12 were male and 16 female. A total of 1225.7 measurement days were recorded from all participants and the algorithms detected 2.66 falls per day. During the study period 15 falls occurred and 12 of these falls were correctly recognized by the fall detection system.

Conclusion

Current fall detection technologies work well under laboratory conditions but it is still problematic to produce reliable results when these technologies are applied to real life conditions. Acceptance towards the sensors decreased after study participation although the system was generally perceived as useful or very useful.

Zusammenfassung

Hintergrund

Stürze stellen eine erhebliche Gefahr für die Gesundheit älterer Menschen und eine Belastung für das Gesundheitssystem dar. Mit der Zunahme der älteren Bevölkerung in den meisten Gesellschaften gewinnt die Sturzerkennung an Bedeutung. Bereits existierende Sturzerkennungssysteme erzielen noch keine verlässlichen Ergebnisse.

Ziel

Sensorengestützte Sturzerkennung, Sturzanalyse mithilfe von Sturzprotokollen sowie Beurteilung der Nutzerakzeptanz hinsichtlich sensorgestützter Sturzerkennungstechnologie durch Fragebogen.

Methoden

Es wurden 28 Senioren zufällig aus einer selbstständig lebenden deutschen Population ausgewählt. Primäres Ziel war die sensorengestützte Sturzerkennung unter Zuhilfenahme von Akzelerometern, Videokameras und Mikrofonen. Details der Stürze wurden mithilfe von medizinisch-geriatrischen Assessments und standardisierten Sturzprotokollen analysiert. Die Studiendauer betrug 8 Wochen und erforderte höchstens 9 Besuche/Studienteilnehmer.

Ergebnis

An der Studie nahmen 28 Testpersonen teil [Alter: Mittelwert 74,3 Jahre, Standardabweichung (SD)  ± 6,3 Jahre, 12 männlich, 16 weiblich]. Insgesamt wurden von allen Probanden 1225,7 Messtage aufgezeichnet und 2,66 Stürze/Tag durch den Algorithmus erkannt. Während der Studie traten 15 Stürze auf; zwölf davon wurden korrekt identifiziert.

Zusammenfassung

Gegenwärtige Sturzerkennungstechnologien weisen gute Resultate unter Laborbedingungen auf, jedoch ergeben sich im Rahmen der Übertragung in reale Bedingungen noch Schwierigkeiten beim Erstellen verlässlicher Ergebnisse. Die Akzeptanz gegenüber den Sensoren ließ nach der Studienteilnahme nach, jedoch wurde das System als „nützlich“ oder „sehr nützlich“ wahrgenommen.

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Compliance with ethical guidelines

Acknowledgement

The Lower Saxony research network “Design of Environments for Ageing” acknowledges the support of the Lower Saxony Ministry of Science and Culture through the “Niedersächsisches Vorab” grant program (grant ZN 2701).

Conflicts of interests

F. Feldwieser, M. Gietzelt, M. Goevercin, M. Marschollek, M. Meis, S. Winkelbach, K.H. Wolf, J. Spehr and E. Steinhagen-Thiessen state that there are no conflicts of interest.

All studies on humans described in the present manuscript were carried out with the approval of the responsible ethics committee and in accordance with national law and the Helsinki Declaration of 1975 (in its current, revised form). Informed consent was obtained from all patients included in studies.

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Authors and Affiliations

  1. Geriatrics Research Group, Department of Geriatric Medicine, Charité Universitätsmedizin Berlin, Reinickendorfer Str. 61, 13447, Berlin, Germany

    Florian Feldwieser, Mehmet Goevercin & Elisabeth Steinhagen-Thiessen

  2. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Braunschweig, Germany

    Matthias Gietzelt & Klaus Hendrik Wolf

  3. Hearing Centre Oldenburg GmbH, Oldenburg, Germany

    Markus Meis

  4. Institute for Robotic and Process Informatics, University of Braunschweig, Braunschweig, Germany

    Simon Winkelbach & Jens Spehr

  5. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School, Hannover, Germany

    Michael Marschollek

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  1. Florian Feldwieser
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  2. Matthias Gietzelt
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Correspondence to Florian Feldwieser.

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Feldwieser, F., Gietzelt, M., Goevercin, M. et al. Multimodal sensor-based fall detection within the domestic environment of elderly people. Z Gerontol Geriat 47, 661–665 (2014). https://doi.org/10.1007/s00391-014-0805-8

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  • DOI: https://doi.org/10.1007/s00391-014-0805-8

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