A Model for Detecting Balance Impairment and Estimating Falls Risk in the Elderly
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Traumatic falls are a prevalent and costly threat to elderly adults. Accurate risk assessment is necessary for reducing incidence of falls. The objective of this study was to test the feasibility of a balance impairment detection model using tasks of sample categorization and falls risk estimation. Model design included an artificial neural network and a statistical discrimination method. The first system produced an individual categorization value, which was then assessed in the second system for relative risk of falls, compared to a normative distribution of healthy elderly peers. Input data included leg muscle electromyographic amplitudes, temporal-distance measures of gait, and medio-lateral measures of whole body center of mass motion. These input data were compiled from a sample of healthy elderly adults (n = 19) and a sample with impaired balance (n = 10) to develop and test the model. Accuracy of sample categorization was assessed using a relative operating characteristic (ROC) value. For relative risk estimation, categorical delineation of risk level was adopted. Sample categorization results reached ROC values of 0.890. Relative risk was frequently assessed at high or very high risk for experiencing falls. Temporal-distance measures were most influential in categorization accuracy, producing the most consistent risk estimates. Combined inputs further improved model performance. This model shows potential for detecting balance impairment and estimating falls risk; thereby indicating need for referral for falls prevention intervention.
- American Academy of Orthopaedic Surgeons. Don’t let a FALL be your last TRIP. 1998.
- Chau, T. A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods. Gait Posture 13:102–120, 2001. CrossRef
- Chou, L.-S., K. R. Kaufman, R. H. Brey, and L. F. Draganich. Motion of the whole body’s center of mass when stepping over obstacles of different heights. Gait Posture 13:17–26, 2001. CrossRef
- Chou, L. S., K. R. Kaufman, M. E. Hahn, and R. H. Brey. Medio-lateral motion of the center of mass during obstacle crossing distinguishes elderly individuals with imbalance. Gait Posture 18:125–133, 2003. CrossRef
- Coogler, C. E. Falls and imbalance. Rehab. Manag. (April/May), 53, 1992.
- Graafmans, W. C., M. E. Ooms, H. M. Hofstee, P. D. Bezemer, L. M. Bouter, and P. Lips. Falls in the elderly: A prospective study of risk factors and risk profiles. Am. J. Epidemiol. 143:1129–1136, 1996.
- Hahn, M. E., and L. S. Chou. Can motion of individual body segments identify dynamic instability in the elderly? Clin. Biomech. 18:737–744, 2003. CrossRef
- Hahn, M. E., A. M. Farley, V. Lin, and L. S. Chou. Neural network estimation of balance control during locomotion. J. Biomech. 38:717–724, 2005. CrossRef
- Hahn, M. E., H. J. Lee, and L. S. Chou. Increased muscular challenge in older adults during obstructed gait. Gait Posture, in press.
- Halfon, P., Y. Eggli, G. Van Melle, and A. Vagnair. Risk of falls for hospitalized patients: A predictive model based on routinely available data. J. Clin. Epidemiol. 54:1258–1266, 2001. CrossRef
- Haykin, S. Neural Networks: A Comprehensive Foundation. New York: MacMillan College Publishing Co, 1994.
- Holzreiter, S. H., and M. E. Kohle. Assessment of gait patterns using neural networks. J. Biomech. 26:45–651, 1993. CrossRef
- Jian, Y., D. A. Winter, M. G. Ishac, and L. Gilchrist, Trajectory of the body COG and COP during initiation and termination of gait. Gait Posture 1:9–22, 1993. CrossRef
- Izumi, K., K. Makimoto, M. Kato, and T. Hiramatsu. Prospective study of fall risk assessment among institutionalized elderly in Japan. Nursing Health Sci. 4:141–147, 2002. CrossRef
- Lafuente, R., J. M. Belda, J. Sanchez-Lacuesta, C. Soler, and J. Prat. Design and test of neural networks and statistical classifiers in computer-aided movement analysis: A case study on gait analysis. Clin. Biomech. 13:216–229, 1998. CrossRef
- Levenberg, K. A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2:164–168, 1944.
- Maki, B. E., P. J. Holliday, and A. K. Topper. A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. J. Gerontol. 49:M72–M84, 1994.
- Marquardt, D. W. An algorithm for least squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11:431–441, 1963. CrossRef
- Meglan, D. A. Enhanced Analysis of Human Locomotion, Ph.D. Dissertation, The Ohio State University, OH, USA, 1991.
- Prentice, S. D., A. E. Patla, and D. A. Stacey. Simple artificial neural network models can generate basic muscle activity patterns for human locomotion at different speeds. Exp. Brain Res. 123:474–480, 1998. CrossRef
- Prentice, S. D., A. E. Patla, and D. A. Stacey. Artificial neural network model for the generation of muscle activation patterns for human locomotion. J. Electromyo. Kinesiol. 11:19–30, 2001. CrossRef
- Province, M. A. The effects of exercise on falls in elderly patients: A preplanned meta-analysis of the FICSIT trials. JAMA 273: 1341–1347, 1995. CrossRef
- Rumelhart, D. E., G. E. Hinton, and R. J. Williams. Learning representations by back-propagation errors. Nature 323:533–536, 1986. CrossRef
- Savelberg, H. H., and A. L. de Lange. Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks. Clin. Biomech. 14:585–92, 1999. CrossRef
- Sepulveda, F., D. M. Wells, and C. L. Vaughan. A neural network representation of electromyography and joint dynamics in human gait. J. Biomech. 26:101–109, 1993. CrossRef
- Shumway-Cook, A., M. Baldwin, N. L. Polissar, and W. Gruber. Predicting the probability of falls in community-dwelling older adults. Phys. Ther. 77:812–819, 1997.
- Shumway-Cook, A., S. Brauer, and M. Woollacott. Predicting the probability of falls in community-dwelling older adults using the Timed Up & Go test. Phys. Ther. 80:896–903, 2000.
- Stalenhoef, P. A., J. P. M. Diedriks, J. A. Knottnerus, A. D. M. Kester, and H. F. J. M. Crebholder. A risk model for the prediction of recurrent falls in community-dwelling elderly: A prospective cohort study. J. Clin. Epidemiol. 55:1088–1094, 2002. CrossRef
- Su, F.-C., and W.-L. Wu. Design and testing of a genetic algorithm neural network in the assessment of gait patterns. Med. Eng. Phys. 22:67–74, 2000. CrossRef
- Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240:1285–1293, 1988.
- Topper, A. K., B. E. Maki, and P. J. Holliday. Are activity-based assessments of balance and gait in the elderly predictive of risk of falling and/or type of fall? J. Am. Geriatr. Soc. 41:479–487, 1993.
- Tromp, A. M., S. M. F. Pluijm, J. H. Smit, D. J. H. Deeg, L. M. Bouter, and P. Lips. Fall-risk screening test: A prospective study on predictors for falls in community-dwelling elderly. J. Clin. Epidemiol. 54:837–844, 2001. CrossRef
- Wolfson, L., R. Whipple, C. Derby, J. Judge, M. King, P. Amerman, J. Schmidt, and D. Smyers. Balance and strength training in older adults: Intervention gains and Tai Chi maintenance. J. Am. Ger. Soc. 44: 498–506, 1996.
- Woltring, H. J. A FORTRAN package for generalized, cross-validatory spline smoothing and differentiation. Adv. Eng. Software 8:104–113, 1986. CrossRef
- A Model for Detecting Balance Impairment and Estimating Falls Risk in the Elderly
Annals of Biomedical Engineering
Volume 33, Issue 6 , pp 811-820
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- Artificial neural network
- Sample categorization
- Relative Risk
- Industry Sectors
- Author Affiliations
- 1. Department of Human Physiology, University of Oregon, Eugene, Oregon, 97403
- 2. Department of Health and Human Development, Montana State University, Bozeman, Montana, 59717
- 3. Department of Human Physiology, University of Oregon, 1240, Eugene, Oregon, 97403