Background: About a third of home-dwelling older people fall each year, and institutionalized older people even report a two- or threefold higher rate for falling. Automatic fall detection systems have been developed to support the independent and secure living of the elderly. Even though good fall detection sensitivity and specificity in laboratory settings have been reported, knowledge about the sensitivity and specificity of these systems in real-life conditions is still lacking. Objective: The aim of this study was to evaluate the long-term fall detection sensitivity and false alarm rate of a fall detection prototype in real-life use. Methods: A total of 15,500 h of real-life data from 16 older people, including both fallers and nonfallers, were monitored using an accelerometry-based sensor system with an implemented fall detection algorithm. Results: The fall detection system detected 12 out of 15 real-life falls, having a sensitivity of 80.0%, with a false alarm rate of 0.049 alarms per usage hour with the implemented real-time system. With minor modification of data analysis the false alarm rate was reduced to 0.025 false alarms per hour, equating to 1 false fall alarm per 40 usage hours. Conclusion: These data suggest that automatic accelerometric fall detection systems might offer a tool for improving safety among older people.

Keywords:
Accelerometer, Fall detector, Specificity, Older people, Frequent faller
1.
Tinetti ME, Speechley M, Ginter SF: Risk factors for falls among elderly persons living in the community. N Engl J Med 1988;319:1701-1707.
2.
Luukinen H, Koski K, Hiltunen L, Kivelä SL: Incidence rate of falls in an aged population in northern Finland. J Clin Epidemiol 1994;47:843-850.
3.
Campbell AJ, Borrie MJ, Spears GF, Jackson SL, Brown JS, Fitzgerald J: Circumstances and consequences of falls experienced by a community population 70 years and over during a prospective study. Age Ageing 1990;19:136-141.
4.
Jensen J, Lundin-Olsson L, Nyberg L, Gustafson Y: Falls among frail older people in residential care. Scand J Public Health 2002;30:54-61.
5.
Fletcher PC, Hirdes JP: Risk factors for falling among community-based seniors using home care services. J Gerontol A Biol Sci Med Sci 2002;57:M504-M510.
6.
Pluijm SM, Smit JH, Tromp EA, Stel VS, Deeg DJ, Bouter LM, et al: A risk profile for identifying community-dwelling elderly with a high risk of recurrent falling: results of a 3-year prospective study. Osteoporos Int 2006;17:417-425.
7.
Fleming J, Matthews FE, Brayne C; Cambridge City over-75s Cohort (CC75C) Study Collaboration: Falls in advanced old age: recalled falls and prospective follow-up of over-90-year-olds in the Cambridge City over-75s Cohort Study. BMC Geriatr 2008;8:6.
8.
Lehtola S, Koistinen P, Luukinen H: Falls and injurious falls late in home-dwelling life. Arch Gerontol Geriatr 2006;42:217-224.
9.
Melander-Wikman A, Jansson M, Hallberg J, Mortberg C, Gard G: The lighthouse alarm and locator trial - a pilot study. Technol Health Care 2007;15:203-212.
10.
Bueno-Cavanillas A, Padilla-Ruiz F, Jimenez-Moleon JJ, Peinado-Alonso CA, Galvez-Vargas R: Risk factors in falls among the elderly according to extrinsic and intrinsic precipitating causes. Eur J Epidemiol 2000;16:849-859.
11.
Fleming J, Brayne C; Cambridge City over-75s Cohort (CC75C) Study Collaboration: Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90. BMJ 2008;337:a2227.
12.
Heinbuchner B, Hautzinger M, Becker C, Pfeiffer K: Satisfaction and use of personal emergency response systems. Z Gerontol Geriatr 2010;43:219-223.
13.
Noury N, Rumeau P, Bourke AK, ÓLaighin G, Lundy JE: A proposal for the classification and evaluation of fall detectors. IRBM 2008;29:340-349.
14.
Bourke AK, O'Brien JV, Lyons GM: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 2007;26:194-199.
15.
Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed 2006;10:156-167.
16.
Lindemann U, Hock A, Stuber M, Keck W, Becker C: Evaluation of a fall detector based on accelerometers: a pilot study. Med Biol Eng Comput 2005;43:548-551.
17.
Benocci M, Tacconi C, Farella E, Benini L, Chiari L, Vanzago L: Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectronics J 2010;41:703-710.
18.
Tamura T: Wearable accelerometer in clinical use. Conf Proc IEEE Eng Med Biol Soc 2005;7:7165-7166.
19.
Bourke AK, van de Ven PW, Chaya AE, OLaighin GM, Nelson J: Testing of a long-term fall detection system incorporated into a custom vest for the elderly. Conf Proc IEEE Eng Med Biol Soc 2008;2008:2844-2847.
20.
Bourke AK, van de Ven P, Gamble M, O'Connor R, Murphy K, Bogan E, et al: Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 2010;43:3051-3057.
21.
Robinovitch SN, Brumer R, Maurer J: Effect of the ‘squat protective response' on impact velocity during backward falls. J Biomech 2004;37:1329-1337.
22.
Kangas M, Vikman I, Nyberg L, Korpelainen R, Lindblom J, Jämsä T: Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture 2012;35:500-505.
23.
Klenk J, Becker C, Lieken F, Nicolai S, Maetzler W, Alt W, et al: Comparison of acceleration signals of simulated and real-world backward falls. Med Eng Phys 2011;33:368-373.
24.
Kangas M, Konttila A, Lindgren P, Winblad I, Jämsä T: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 2008;28:285-291.
25.
Kangas M, Konttila A, Winblad I, Jämsä T: Determination of simple threshold for accelerometry-based parameters for fall detection. Conf Proc IEEE Eng Med Biol Soc 2007;2007:1367-1370.
26.
Kangas M, Vikman I, Wiklander J, Lindgren P, Nyberg L, Jämsä T: Sensitivity and specificity of fall detection in people aged 40 years and over. Gait Posture 2009;29:571-574.
27.
Folstein MF, Folstein SE, McHugh PR: ‘Mini-mental state'. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:1891-1898.
28.
Katz S, Ford AB, Moskowitz RW, Jaffe MW: Studies of illness in the aged. The index of ADL: a standardized measure of biological and psychosocial function. J Am Med Assoc 1963;185:914-919.
29.
Podsiadlo D, Richardson S: The timed ‘Up & Go': a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991;39:142-148.
30.
Bloch F, Gautier V, Noury N, Lundy JE, Poujaud J, Claessens YE, et al: Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects. Ann Phys Rehabil Med 2011;54:391-398.
31.
Bagalá F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, et al: Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One 2012;7:e37062.
32.
Holden RJ, Karsh B: The Technology Acceptance Model: its past and its future in health care. J Biomed Informatics 2010;43:159-172.
33.
Gibson MJ, Andres RO, Isaacs B, Radebaugh T, Worm-Petersen J: The prevention of falls in later life. A report of the Kellogg International Work Group on the prevention of falls by the elderly. Danish Med Bullet 1987;34(suppl 4):1-24.
34.
Becker C, Schwickert L, Mellone S, Bagala F, Chiari L, Helbostad JL, et al: Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors. Z Gerontol Geriatr 2012;45:707-715.
35.
Chao PK, Chan HL, Tang FT, Chen YC, Wong MK: A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration. Physiol Meas 2009;30:1027-1037.
36.
Lord SR, Sherrington C, Menz H, Close J: Falls in older people. Risk factors and strategies for prevention. Cambridge, Cambridge University Press, 2007.
© 2014 S. Karger AG, Basel
2014
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.