Automatic Fall Risk Estimation using the Nintendo Wii Balance Board

Gert Mertes, Greet Baldewijns, Pieter-Jan Dingenen, Tom Croonenborghs, Bart Vanrumste


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.


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Paper Citation

in Harvard Style

Mertes G., Baldewijns G., Dingenen P., Croonenborghs T. and Vanrumste B. (2015). Automatic Fall Risk Estimation using the Nintendo Wii Balance Board . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 75-81. DOI: 10.5220/0005208700750081

in Bibtex Style

author={Gert Mertes and Greet Baldewijns and Pieter-Jan Dingenen and Tom Croonenborghs and Bart Vanrumste},
title={Automatic Fall Risk Estimation using the Nintendo Wii Balance Board},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Automatic Fall Risk Estimation using the Nintendo Wii Balance Board
SN - 978-989-758-068-0
AU - Mertes G.
AU - Baldewijns G.
AU - Dingenen P.
AU - Croonenborghs T.
AU - Vanrumste B.
PY - 2015
SP - 75
EP - 81
DO - 10.5220/0005208700750081