A clinical study to assess fall risk using a single waist accelerometer.
Falls have various causes and are often associated with mobility impairments. Preventive steps to avoid falls may be initiated, if an increasing fall risk could be detected in time. The objective of this article is to identify an automated sensor-based method to determine fall risk of patients based on objectively measured gait parameters. One hundred fifty-one healthy subjects and 90 subjects at risk of falling were measured during a Timed 'Up & Go' test with a single triaxial acceleration sensor worn on a waist belt. The fall risk was assessed using the STRATIFY score. A decision tree induction algorithm was used to distinguish between subjects with high and low risk using the determined gait parameters. The results of the risk classification produce an overall accuracy of 90.4% in relation to STRATIFY score. The sensitivity amount to 89.4%, the specificity to 91.0% and the reliability parameter kappa equals 0.79. The method presented is able to distinguish between subjects with high and low fall risk. It is unobtrusive and therefore may be applied over extended time periods. A subsequent study is needed to confirm the model's suitability for data recorded in patients' everyday lives.
Gietzelt
Matthias
M
Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Muehlenpfordtstrasse 23, D-38106 Braunschweig, Germany. matthias.gietzelt@plri.de
Nemitz
Gerhard
G
Wolf
Klaus-Hendrik
KH
Meyer Zu Schwabedissen
Hubertus
H
Haux
Reinhold
R
Marschollek
Michael
M
D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000369:Aged, 80 and over; D005858:Germany; D006801:Humans; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D011446:Prospective Studies; D018570:Risk Assessment / Q000379:methods