Review of fall risk assessment in geriatric populations using inertial sensors

Jennifer Howcroft, Jonathan Kofman, Edward D Lemaire, Jennifer Howcroft, Jonathan Kofman, Edward D Lemaire

Abstract

Background: Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.

Methods: Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.

Results: Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).

Conclusions: Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

Figures

Figure 1
Figure 1
Summary of literature search process.

References

    1. O’Sullivan M, Blake C, Cunningham C, Boyle G, Finucane C. Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age Ageing. 2009;38:308–313.
    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. doi: 10.1056/NEJM198812293192604.
    1. Masud T, Morris RO. Epidemiology of falls. Age Ageing. 2001;30:3–7.
    1. Axer H, Axer M, Sauer H, Witte OW, Hagemann G. Falls and gait disorders in geriatric neurology. Clin Neurol Neurosurg. 2010;112:265–274. doi: 10.1016/j.clineuro.2009.12.015.
    1. Rao SS. Prevention of falls in older patients. Am Fam Physician. 2005;72:81–88.
    1. Stevens JA, Corso PS, Finkelstein EA, Miller TR. The costs of fatal and non-fatal falls among older adults. Inj Prev. 2006;12:290–295. doi: 10.1136/ip.2005.011015.
    1. Englander F, Hodson TJ, Terregrossa RA. Economic dimensions of slip and fall injuries. J Forensic Sci. 1996;41:733–746.
    1. Hart-Hughes S, Quigley P, Bulat T, Palacios P, Scott S. An interdisciplinary approach to reducing fall risks and falls. J Rehabil. 2004;70:46–51.
    1. Canavan PK, Cahalin LP, Lowe S, Fitzpatrick D, Harris M, Plummer-D’Amato P. Managing gait disorders in older persons residing in nursing homes: a review of literature. J Am Med Dir Assoc. 2009;10:230–237. doi: 10.1016/j.jamda.2009.02.008.
    1. Maki BE, Sibley KM, Jaglal SB, Bayley M, Brooks D, Fernie GR, Flint AJ, Gage W, Liu BA, McIlroy WE, Mihailidis A, Perry SD, Popovic MR, Pratt J, Zettel JL. Reducing fall risk by improving balance control: development, evaluation, and knowledge-translation of new approaches. J Safety Res. 2011;42:473–485. doi: 10.1016/j.jsr.2011.02.002.
    1. Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82:1050–1056. doi: 10.1053/apmr.2001.24893.
    1. Finlayson ML, Peterson EW. Falls, aging, and disability. Phys Med Rehabil Clin N Am. 2010;21:357–373. doi: 10.1016/j.pmr.2009.12.003.
    1. Culhane KM, O’Connor M, Lyons D, Lyons GM. Accelerometers in rehabilitation medicine for older adults. Age Ageing. 2005;34:556–560. doi: 10.1093/ageing/afi192.
    1. Lee RYW, Carlisle AJ. Detection of falls using accelerometers and mobile phone technology. Age Ageing. 2011;40:690–696. doi: 10.1093/ageing/afr050.
    1. American Geriatrics Society. AGS/BGS Clinical Practice Guideline: Prevention of Falls in Older Persons. 2010. [ ]
    1. Weinstein M, Booth J. Preventing falls in older adults: a multifactorial approach. Home Health Care Manag and Pract. 2006;19:45–50. doi: 10.1177/1084822306292232.
    1. Najafi B, Aminian K, Loew F, Blanc Y, Robert PA. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng. 2002;49:843–851. doi: 10.1109/TBME.2002.800763.
    1. Cho C-Y, Kamen G. Detecting balance deficits in frequent fallers using clinical and quantitative evaluation tools. J Am Geriatr Soc. 1998;46:426–430.
    1. Hahn ME, Chou L-S. Can motion of individual body segments identify dynamic instability in the elderly? Clin Biomech. 2003;18:737–744. doi: 10.1016/S0268-0033(03)00139-6.
    1. Maki BE. Biomechanical approach to quantifying anticipatory postural adjustments in the elderly. Med Bio Eng and Comput. 1993;31:355–362. doi: 10.1007/BF02446688.
    1. Nelson-Wong E, Appell R, McKay M, Nawaz H, Roth J, Sigler R, Third J, Walker M. Increased fall risk is associated with elevated co-contraction about the ankle during static balance challenges in older adults. Eur J Appl Physiol. 2012;112:1379–1389. doi: 10.1007/s00421-011-2094-x.
    1. Mathie MJ, Coster AC, Lovell NH, Celler BG. Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol Meas. 2004;25:R1–R20. doi: 10.1088/0967-3334/25/2/R01.
    1. Preece SJ, Goulermas JY, Kenney LP, Howard D, Meijer K, Crompton R. Activity identification using body-mounted sensors-a review of classification techniques. Physiol Meas. 2009;30:R1–R33. doi: 10.1088/0967-3334/30/4/R01.
    1. Taraldsen K, Chastin SF, Riphagen II, Vereijken B, Helbostad JL. Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas. 2012;71:13–19. doi: 10.1016/j.maturitas.2011.11.003.
    1. Yang C-C, Hsu Y-L. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors. 2010;10:7772–7788. doi: 10.3390/s100807772.
    1. Shany T, Redmond SJ, Narayanan MR, Lovell NH. Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sensors J. 2012;12:658–670.
    1. Shany T, Redmond SJ, Marschollek M, Lovell NH. Assessing fall risk using wearable sensors: a practical discussion. Z Gerontol Geriatr. 2012;45:694–706. doi: 10.1007/s00391-012-0407-2.
    1. Lalkhen AG, McCluskey A. Clinical tests: sensitivity and specificity. Contin Educ in Anaesth, Crit Care and Pain. 2008;8:221–223. doi: 10.1093/bjaceaccp/mkn041.
    1. Mahoney FI, Barthel DW. Functional evaluation: the barthel index. Md State Med J. 1965;14:56–61.
    1. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA. Frailty in older adults: evidence for a phenotype. J Gerontology: Med Sci. 2001;56A:M146–M156.
    1. Honaker JA, Boismier TE, Shepard NP, Shepard NT. Fukuda stepping test: sensitivity and specificity. J Am Acad Audiol. 2009;20:311–314. doi: 10.3766/jaaa.20.5.4.
    1. Mourey F, Camus A, D’Athis P, Blanchon M-A, Martin-Hunyadi C, de Rekeneire N, Pfitzenmeyer P. Mini motor test: a clinical test for rehabilitation of patients showing psychomotor disadaptation syndrome. Arch Gerontol Geriatr. 2005;40:201–211. doi: 10.1016/j.archger.2004.08.004.
    1. Hurvitz EA, Richardson JK, Werner RA, Ruhl AM, Dixon MR. Unipedal stance testing as an indicator of fall risk among older outpatients. Arch Phys Med Rehabil. 2000;81:587–591. doi: 10.1016/S0003-9993(00)90039-X.
    1. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB. A short physical performance battery assessing lower-extremity function: association with self-reported disability and prediction of mortality and nursing-home admission. J of Gerontology. 1994;49:M85–M94. doi: 10.1093/geronj/49.2.M85.
    1. Lord SR, Menz HB, Tiedemann A. A physiological profile approach to falls risk assessment and prevention. Phys Ther. 2003;83:237–252.
    1. Oliver D, Britton M, Seed P, Martin FC, Hopper AH. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case–control and cohort studies. Br Med J. 1997;315:1049–1053. doi: 10.1136/bmj.315.7115.1049.
    1. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the timed up and go test. Phys Ther. 2000;80:896–903.
    1. Tinetti ME, Williams TF, Mayewski R. Fall index for elderly patients based on number of chronic disabilities. Am J Med. 1986;80:429–434. doi: 10.1016/0002-9343(86)90717-5.
    1. Auvinet B, Berrut G, Touzard C, Moutel L, Collet N, Chaleil D, Barrey E. Gait abnormalities in elderly fallers. J Aging Phys Act. 2003;11:40–52.
    1. Bautmans I, Jansen B, van Keymolen B, Mets T. Reliability and clinical correlates of 3D-accelerometry based gait analysis outcomes according to age and fall-risk. Gait Posture. 2011;33:366–372. doi: 10.1016/j.gaitpost.2010.12.003.
    1. Caby B, Kieffer S, Hubert MS, Cremer G, Macq B. Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry. Biomed Eng Online. 2011;10:1. doi: 10.1186/1475-925X-10-1.
    1. Doheny EP, Fan CW, Foran T, Greene BR, Cunningham C, Kenny RA. 33rd Annual International Conference of the IEEE EMBS: 30 August-3 September 2011. Boston; 2011. An instrumented sit-to-stand used to examine differences between older fallers and non-fallers; pp. 3063–3066.
    1. Doheny EP, McGarth D, Greene BR, Walsh L, McKeown D, Cunningham C, Crosby L, Kenny RA, Caulfield B. 34th Annual International Conference of the IEEE EMBS: 28 August-1 September 2012. San Diego; 2012. Displacement of centre of mass during quiet standing assessed using accelerometry in older fallers and non-fallers; pp. 3300–3303.
    1. Doi T, Hirata S, Ono R, Tsutsumimoto K, Misu S, Ando H. The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study. J Neuroeng Rehabil. 2013;10:7. doi: 10.1186/1743-0003-10-7.
    1. Ganea R, Paraschiv-Ionescu A, Büla C, Rochat S, Aminian K. Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people. Med Eng and Physics. 2011;33:1086–1093. doi: 10.1016/j.medengphy.2011.04.015.
    1. Giansanti D. Investigation of fall-risk using a wearable device with accelerometers and rate gyroscopes. Physiol Measures. 2006;27:1081–1090. doi: 10.1088/0967-3334/27/11/003.
    1. Giansanti D, Maccioni G, Cesinaro S, Benvenuti F, Macellari V. Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography. Med Eng and Physics. 2008;30:367–372. doi: 10.1016/j.medengphy.2007.04.006.
    1. Giansanti D, Macellari V, Maccioni G. New neural network classifier of fall-risk based on the mahalanobis distance and kinematic parameters assessed by a wearable device. Physiol Measures. 2008;29:N11–N19. doi: 10.1088/0967-3334/29/3/N01.
    1. Gietzelt M, Nemitz G, Wolf K-H, Meyer Zu Schwabedissen H, Haux R, Marschollek M. A clinical study to assess fall risk using a single waist accelerometer. Inform Health Soc Care. 2009;34:181–188. doi: 10.3109/17538150903356275.
    1. Greene BR, O’Donovan A, Romero-Ortuno R, Cogan L, Scanaill CN, Kenny RA. Quantitative falls risk assessment using the timed up and go test. IEEE Trans Biomed Eng. 2010;57:2918–2926.
    1. Greene BR, McGrath D, Walsh L, Doheny EP, McKeown D, Garattini C, Cunningham C, Crosby L, Caulfield B, Kenny RA. Quantitative falls risk estimation through multi-sensor assessment of standing balance. Physiol Meas. 2012;33:2049–2063. doi: 10.1088/0967-3334/33/12/2049.
    1. Ishigaki N, Kimura T, Usui Y, Aoki K, Narita N, Shimizu M, Hara K, Ogihara N, Nakamura K, Kato H, Ohira M, Yokokawa Y, Miyoshi K, Murakami N, Okada S, Nakamura T, Saito N. Analysis of pelvic movement in the elderly during walking using a posture monitoring system equipped with a triaxial accelerometer and a gyroscope. J Biomech. 2011;44:1788–1792. doi: 10.1016/j.jbiomech.2011.04.016.
    1. Kojima M, Obuchi S, Henmi O, Ikeda N. Comparison of smoothness during gait between community dwelling elderly fallers and non-fallers using power spectrum entropy of acceleration time-series. J Phys Ther Sci. 2008;20:243–248. doi: 10.1589/jpts.20.243.
    1. Laessoe U, Hoeck HC, Simonsen O, Sinkjaer T, Voigt M. Fall risk in an active elderly population-can it be assessed? J Negat Results Biomed. 2007;6:2. doi: 10.1186/1477-5751-6-2.
    1. Latt MD, Menz HB, Fung VS, Lord SR. Acceleration patterns of the head and pelvis during gait in older people with Parkinson’s disease: a comparison of fallers and nonfallers. J Gerontology: Med Sci. 2009;64A:700–706. doi: 10.1093/gerona/glp009.
    1. Liu J, Lockhart TE, Jones M, Martin T. Local dynamic stability assessment of motion impaired elderly using electronic textile pants. IEEE Trans Autom Sci Eng. 2008;5:696–702.
    1. Liu Y, Redmond SJ, Narayanan MR, Lovell NH. 33rd Annual International Conference of IEEE EMBS: 30 August-3 September 2011. Boston; 2011. Classification between non-multiple fallers and multiple fallers using a triaxial accelerometry-based system; pp. 1499–1502.
    1. Liu Y, Redmond SJ, Wang N, Blumenkron F, Narayanan MR, Lovell NH. Spectral analysis of accelerometry signals from a directed-routine for falls-risk estimation. IEEE Trans Biomed Eng. 2011;58:2308–2315.
    1. Marschollek M, Wolf K-H, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H, Haux R. 30th Annual International IEEE EMBS Conference: 20-24 August 2008. Vancouver; 2008. Assessing elderly persons’ fall risk using spectral analysis on accelerometric data-a clinical evaluation study; pp. 3682–3685.
    1. Marschollek M, Nemitz G, Gietzelt M, Wolf KH, Wolf KH, Meyer Zu Schwabedissen H, Haux R. Predicting in-patient falls in a geriatric clinic. Z Gerontol Geriatr. 2009;4:317–322.
    1. Marschollek M, Rehwald A, Wolf K-H, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H, Haux R. Sensor-based fall risk assessment-an expert ‘to go’. Methods Inf Med. 2011;50:420–426. doi: 10.3414/ME10-01-0040.
    1. Marschollek M, Rehwald A, Wolf K-H, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H, Schulze M. Sensors vs. experts-a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients. BMC Med Inform Decis Mak. 2011;11:48. doi: 10.1186/1472-6947-11-48.
    1. Martínez-Ramírez A, Lecumberri P, Gómez M, Rodriguez-Mañas L, García FJ, Izquierdo M. Frailty assessment based on wavelet analysis during quiet standing balance test. J Biomech. 2011;44:2213–2220. doi: 10.1016/j.jbiomech.2011.06.007.
    1. Menz HB, Lord SR, Fitzpatrick RC. Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. J of Gerontology: Med Sci. 2003;58A:446–452.
    1. Moe-Nilssen R, Helbostad JL. Interstride trunk acceleration variability but not step width variability can differentiate between fit and frail older adults. Gait Posture. 2005;21:164–170. doi: 10.1016/j.gaitpost.2004.01.013.
    1. Narayanan MR, Scalzi ME, Redmond SJ, Lord SR, Celler BG, Lovell NH. 30th Annual International IEEE EMBS Conference: 20-24 August 2008. Vancouver; 2008. A wearable triaxial accelerometry system for longitudinal assessment of falls risk; pp. 2840–2843.
    1. Narayanan MR, Scalzi ME, Redmond SJ, Lord SR, Celler BG, Lovell NH. 31st Annual International Conference of the IEEE EMBS: 2-6 September 2009. Minneapolis; 2009. Evaluation of functional deficits and falls risk in the elderly-methods for preventing falls; pp. 6179–6182.
    1. Narayanan MR, Redmond SJ, Scalzi ME, Lord SR, Celler BG, Lovell NH. Longitudinal falls-risk estimation using triaxial accelerometry. IEEE Trans Biomed Eng. 2010;57:534–541.
    1. Paterson K, Hill K, Lythgo N. Stride dynamics, gait variability and prospective falls risk in active community dwelling older women. Gait Posture. 2011;33:251–255. doi: 10.1016/j.gaitpost.2010.11.014.
    1. Redmond SJ, Scalzi ME, Narayanan MR, Lord SR, Cerutti S, Lovell NH. 32nd Annual International Conference of the IEEE EMBS: August 31-September 4 2010. Buenos Aires; 2010. Automatic segmentation of triaxial accelerometry signals for falls risk estimation; pp. 2234–2237.
    1. Schwesig R, Fischer D, Lauenroth A, Becker S, Leuchte S. Can falls be predicted with gait analytical and posturographic measurement systems? a prospective follow-up study in a nursing home population. Clin Rehabil. 2013;27:183–190. doi: 10.1177/0269215512452880.
    1. Senden R, Savelberg HH, Grimm B, Heyligers IC, Meijer K. Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling. Gait Posture. 2012;36:296–300. doi: 10.1016/j.gaitpost.2012.03.015.
    1. Toebes MJP, Hoozemans MJ, Furrer R, Dekker J, van Dieën JH. Local dynamic stability and variability of gait are associated with fall history in elderly subjects. Gait Posture. 2012;36:527–531. doi: 10.1016/j.gaitpost.2012.05.016.
    1. Weiss A, Herman T, Plotnik M, Brozgol M, Giladi N, Hausdorff JM. An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Physiol Measures. 2011;32:2003–2018. doi: 10.1088/0967-3334/32/12/009.
    1. Yack HJ, Berger RC. Dynamic stability in the elderly: identifying a possible measure. J of Gerontology: Med Sci. 1993;48:M225–M230. doi: 10.1093/geronj/48.5.M225.
    1. Webster JG. The measurement, instrumentation, and sensors handbook. Florida: CRC Press LLC; 1999.
    1. Martin FC. Falls risk factors: assessment and management to prevent falls and fractures. Can J Aging. 2011;30:33–44. doi: 10.1017/S0714980810000747.
    1. Prince F, Corriveau H, Hébert R, Winter DA. Gait in the elderly. Gait Posture. 1997;5:128–135. doi: 10.1016/S0966-6362(97)01118-1.
    1. Maki BE. Gait changes in older adults: predictors of falls or indicators of fear? J Am Geriatr Soc. 1997;45:313–320.
    1. Giansanti D, Morelli S, Maccioni G, Constantini G. Toward the design of a wearable system for fall-risk detection in telerehabilitation. Telemed e-Health. 2009;15:296–299. doi: 10.1089/tmj.2008.0106.
    1. Gemperle F. Design for wearability. Pittsburgh: Second International Symposium on Wearable Computers; 1998. pp. 116–122.
    1. Podsiadlo D, Richardson S. The timed “Up and Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142–148.
    1. Ganz DA, Higashi T, Rubenstein LZ. Monitoring falls in cohort studies of community-dwelling older people: effect of the recall interval. J Am Geriatr Soc. 2005;53:2190–2194. doi: 10.1111/j.1532-5415.2005.00509.x.
    1. Gövercin M, Költzsch Y, Meis M, Wegel S, Gietzelt M, Spehr J, Winkelbach S, Marschollek M, Steinhagen-Thiessen E. Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inform Health Soc Care. 2010;35:177–187. doi: 10.3109/17538157.2010.528648.
    1. Cole MH, Silburn PA, Wood JM, Worringham CJ, Kerr GK. Falls in Parkinson’s disease: kinematic evidence for impaired head and trunk control. Mov Disord. 2010;25:2369–2378. doi: 10.1002/mds.23292.
    1. Wu HH, Lemaire ED, Baddour N. Activity change-of-state identification using a blackberry smartphone. J Med and Biol Eng. 2012;32:265–272. doi: 10.5405/jmbe.967.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel