Automatic Fall Risk Estimation using the Nintendo Wii Balance Board

摘要

In this paper, a tool to assess a person´s fall risk with the Nintendo Wii Balance Board based on Center of Pressure (CoP) recordings is presented. Support Vector Machine and K-Nearest Neighbours classifiers are used to distinguish between people who experienced a fall in the past twelve months and those who have not. The classifiers are trained using data recorded from 39 people containing a mix of students and elderly. Validation is done using 10-fold cross-validation and the classifiers are also validated against additional data recorded from 12 elderly. A cross-validated average accuracy of 96.49% +/- 4.02 is achieved with the SVM classifier with radial basis function kernel and 95.72% +/- 1.48 is achieved with the KNN classifier with k=4. Validation against the additional dataset of 12 elderly results in a maximum accuracy of 76.6% with the linear SVM.)