Abstract
Background
A considerable proportion of falls occur within the domestic environment. Sensor-based identification of falls in seniors’ homes could help them to remain autonomous and self-sufficient in their own homes. The objective of this study was to evaluate fall detection systems within the home environment using optical and accelerometric sensor systems.
Methods
Portable triaxial accelerometers and optical sensors were used to detect falls in subjects with known problems of mobility and a recent fall history.
Results
Three subjects were investigated with the system. Overall nine falls occurred during the study period. Four falls were recorded by the accelerometric system and one fall by the optical system. Subjects with increased risk of falling as measured with mobility and fall risk assessments tend to fall more frequently.
Conclusion
The study shows that there is a considerably large difference between fall-detector evaluation studies in domestic environments and in laboratory trials.
Zusammenfassung
Hintergrund
Die Identifikation von Stürzen mithilfe sensorbasierter Technologien könnte in der Zukunft Menschen helfen, länger selbstständig und selbstbestimmt in ihrem eigenen häuslichen Umfeld zu leben. Ziel der Studie ist die Evaluation von technischen Sturzerkennungssystemen im häuslichen Umfeld durch eine kombinierte Erfassung mithilfe von optischen Sensoren und Beschleunigungssensoren.
Material und Methoden
In der häuslichen Umgebung von Probanden mit bekannten Mobilitätseinschränkungen und Sturzerfahrungen werden optische Sensorsysteme installiert. Zusätzlich wird ein tragbares triaxiales Akzelerometer verwendet.
Ergebnisse
Es wurden drei Probanden mit dem System untersucht. Im Untersuchungszeitraum traten insgesamt 9 Stürze auf. Vier der Stürze konnten mithilfe des Akzelerometers und ein Sturz mithilfe des optischen Sensorsystems aufgezeichnet werden. Tendenziell zeigte es sich, dass diejenigen Probanden, die in Sturzrisikoassessments eine erhöhte Sturzgefahr aufwiesen, auch vermehrt stürzten.
Schlussfolgerung
Mit dem aktuellen Stand der Studie wird deutlich, wie stark sich die Evaluation in einer Feldstudie im realen häuslichen Umfeld von Sturzstudien im Labor unterscheidet.
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References
Bagalà F, Becker C, Cappello A et al (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One 7(5):e37062
Bennell K, Dobson F, Hinman R (2011) Measures of physical performance assessments: Self-Paced Walk Test (SPWT), Stair Climb Test (SCT), Six-Minute Walk Test (6MWT), Chair Stand Test (CST), Timed Up & Go (TUG), Sock Test, Lift and Carry Test (LCT), and Car Task. Arthritis Care Res (Hoboken) 63(Suppl) 11:S350–S370
Bourke AK, Ven PW van de, Chaya AE et al (2008) Testing of a long-term fall detection system incorporated into a custom vest for the elderly. Conf Proc IEEE Eng Med Biol Soc:2844–2847 (Vancouver, Canada)
Bourke AK, Ven PW van de, Gamble M et al (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43(15):3051–3057
Connell BR, Wolf SL (1997) Environmental and behavioral circumstances associated with falls at home among healthy elderly individuals? Arch Phys Med Rehab 78(2):179–186
Cucchiara R, Prati A, Vezzani R (2007) A multi-camera vision system for fall detection and alarm generation. Expert Systems 24(5):334–345
Diraco G, Leone A, Siciliano P (2010) An active vision system for fall detection and posture recognition in elderly healthcare. Design, Automation & Test in Europe Conference & Exhibition (DATE):1536–1541
Duncan PW, Weiner DK, Chandler J, Studenski S (1990) Functional reach: a new clinical measure of balance. J Gerontol 45(6):M192–M197
Englander F, Hodson TJ, Terregrossa RA (1996) Economic dimensions of slip and fall injuries. J Forensic Sci 41(5):733–746
Folstein MF, Folstein SE, McHugh PR (1975) ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J Psych Res 12(3):189–198
Gardner MM, Robertson MC, Campbell AJ (2000) Exercise in preventing falls and fall related injuries in older people: a review of randomised controlled trials. Brit J Sports Med 34(1):7–17
Gövercin M, Költzsch Y, Meis M et al (2010) Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inf Health Soc Care 35(3–4):177–187
Kangas M, Vikman I, Nyberg L et al (2012) Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture 35(3):500–505
King MB, Tinetti ME (1995) Falls in community-dwelling older persons. J Am Ger Soc 43(8):1146–1154
Krulish LH, Anemaet WK (2008) Fally risk assessment & prevention in home care. Home Health Care Manag Pract 20(2):169–174
Lamb SE, Jørstad-Stein EC, Hauer K et al (2005) Development of a common outcome data set for fall injury prevention trials: the prevention of falls network Europe consensus. J Am Ger Soc 53(9):1618–1622
Lord SR, Sherrington C, Menz HB, Close J (2007) Falls in older people: risk factors and strategies for prevention, 2nd edn. Cambridge University Press, Cambridge
Mahoney FI, Barthel DW (1965) Functional evaluation: the Barthel index. Maryland State Med J 21:61–65
Marschollek M, Gövercin M, Wolf KH et al (2008) A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons. Conf Proc IEEE Eng Med Biol Soc:1319–1322 (Vancouver, Canada)
Nait-Charif H, McKenna SJ (2004) Activity summarisation and fall detection in a supportive home environment. Proc Int Conf Pat Recog (ICPR):323–326
Oliver D, Britton M, Seed P et al (1997) Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies. BMJ 315(7115):1049–1053
Pierobon A, Funk M (2004) Sturzprävention bei älteren Menschen. 1st edn. Georg Thieme, Stuttgart
Podsiadlo D, Richardson S (1991) The timed ‘Up & Go’: a test of basic functional mobility for frail elderly persons. J Am Ger Soc 39(6):142–148
Shumway-Cook A, Silver IF, LeMier M et al (2007) Effectiveness of a community-based multifactorial intervention on falls and fall risk factors in community-living older adults: a randomized, controlled trial. J Gerontol A Biol Sci Med Sci 62(12):1420–1427
Shumway-Cook A, Ciol MA, Hoffman J et al (2009) Falls in the Medicare population: incidence, associated factors, and impact on health care. Phys Ther 89(4):1–9
Spehr J, Gövercin M, Winkelbach S et al (2008) Visual fall detection in home environments. Conf Proc Int Soc Gerontechnol (Pisa, Italy):114–118
Stevens JA, Corso PS, Finkelstein EA, Miller TR (2006) The costs of fatal and nonfatal falls among older adults. Inj Prev 12:290–295
Tinetti ME (1986) Performance-oriented assessment of mobility problems in elderly patients. J Am Ger Soc 34:119–126
Yates SM, Dunnagan TA (2001) Evaluating the effectiveness of a home-based fall risk reduction program for rural community-dwelling older adults. J Gerontol A Biol Sci Med Sci 56(4):M226–M230
Acknowledgment
The Lower Saxony research network “Design of Environments for Aging” acknowledges the support of the Lower Saxony Ministry of Science and Culture through the “Niedersächschisches Vorab” grant program (grant ZN 2701).
Conflict of interest
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
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Gietzelt, M., Spehr, J., Ehmen, Y. et al. GAL@Home. Z Gerontol Geriat 45, 716–721 (2012). https://doi.org/10.1007/s00391-012-0400-9
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DOI: https://doi.org/10.1007/s00391-012-0400-9