Quantitative Assessment of Balance Impairment for Fall-Risk Estimation Using Wearable Triaxial Accelerometer

Abstract

Falls are a major cause of morbidity and long-term hospitalization among growing older population. An automated and accurate fall-risk assessment system is vital to identify high fall-risk population and to prevent falls by early intervention. Therefore, this paper provides an objective, cost-effective, and unsupervised method to obtain functional balance and mobility assessment-based fall-risk of community-dwelling older adults. More specifically, waist-mounted triaxial accelerometer signals acquired from directed routine (supervised control movements) are used to estimate the well-known clinical assessment score-Berg balance scale (BBS). The trunk acceleration signals are used to extract features and to find the optimal subset of features for each training data during repeated tenfold cross validation of the BBS estimation model. The average of two BBS estimates based on test and retest yielded a strong correlation $\rho = 0.86$ with the standard BBS score. Also, high correlation ( $\rho = 0.90$ ) and low root-mean-square error (1.66) was observed between the two estimates of each subject. The proposed method is well suited for the assessment of balance impairment and pre-screening of quantitative fall-risk in an unsupervised setting. It has the potential to act as a surrogate of the standard clinical assessment-BBS.