A New Auto-Scoring Algorithm for Balance Assessment with Wearable IMU Device Based on Nonlinear Model

Abstract

In this paper, a new auto-scoring algorithm that automatically evaluates the Berg balance scale (BBS) tasks is proposed. The BBS can be used as an indicator for patients to analyze their rehabilitation status by themselves. In the proposed method, the patient must use a wearable inertial measurement unit (IMU) sensor, and the result of the patient's BBS task execution would be scored automatically by the evaluation algorithm. The proposed evaluation algorithm involves only few computations and has high scoring accuracy. Nonlinear kernel principal component analysis and a small number of linear features were combined to reduce the features from each sensor, and the algorithm model was implemented using a support vector machine (SVM), a machine learning technique with low computational complexity. The effectiveness of the algorithm was evaluated through clinical evaluation of 53 subjects with up to eight IMU sensors. The average accuracy of the proposed algorithm using eight sensors was 93.2%, and that using five sensors was 91.5%. There was a 12.6% and 10.9% increase in accuracy, respectively, compared to a previous study. The training and testing times of the proposed SVM model were over 38 times faster than the multi-layer perceptron model used in a previous study.