Longitudinal Falls-Risk Estimation Using Triaxial Accelerometry

• Published in: IEEE transactions on biomedical engineering 2010-03, Vol.57 (3), p.534-541
• Main Authors: Narayanan, Michael R., Redmond, Stephen J., Scalzi, Maria Elena, Lord, Stephen R., Celler, Branko G., Lovell $^{\ast}$, Nigel H.
• Format: Article
• Language: English
• Subjects: Acceleration; Accelerometers; Accelerometry; Accidental Falls; Aged; Aged, 80 and over; Australia; Biological and medical sciences; Biomedical engineering; Cohort Studies; Costs; falls; falls risk; Feature extraction; Female; Hematology; Hospitals; Humans; Investigative techniques, diagnostic techniques (general aspects); Least squares methods; Longitudinal Studies; Male; Medical sciences; Models, Biological; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Muscles; Older people; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Predictive Value of Tests; Risk; Senior citizens; signal processing; Signal Processing, Computer-Assisted; Testing
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2009.2033038

Falls among the elderly population are a major cause of morbidity and injury-particularly among the over 65 years age group. Validated clinical tests and associated models, built upon assessment of functional ability, have been devised to estimate an individual's risk of falling in the near future. Those identified as at-risk of falling may be targeted for interventative treatment. The migration of these clinical models estimating falls risk to a surrogate technique, for use in the unsupervised environment, might broaden the reach of falls-risk screening beyond the clinical arena. This study details an approach that characterizes the movements of 68 elderly subjects performing a directed routine of unsupervised physical tasks. The movement characterization is achieved through the use of a triaxial accelerometer. A number of fall-related features, extracted from the accelerometry signals, combined with a linear least squares model, maps to a clinically validated measure of falls risk with a correlation of ¿ = 0.81(p < 0.001).