Estimating Berg Balance Scale and Mini Balance Evaluation System Test Scores by Using Wearable Shoe Sensors

Abstract

Measuring humans' functional balance is important for clinical estimation of fall risk. Although many clinical assessments, such as Berg Balance Scale and Mini Balance Evaluation System Test, are available to test the functional balance, the results are affected by the skills of different operators. This paper proposes an objective approach to access the functional balance by a wearable sensor system embedded in the shoe and a hip accelerometer. Support Vector Machine regression models are built with numerical features selected by mRMR algorithm to estimate the scores of the clinical assessments. Leave one out cross validation is employed to evaluate the regression models. The approach is validated on a group of 30 seniors ($76\pm 10.5$ years old), containing fallers and non-fallers. The results show that the wearable sensor system has a capability to estimate the Berg Balance Scale and Mini Balance Evaluation System Test scores with absolute mean errors and standard deviations $6.07\pm 3.76$ and $5.45\pm 3.65$, respectively, and demonstrates high agreement with falls history based risk assessment.