This paper studies the feasibility of using a low-cost game device called Wii Fit Balance Board® to measure the static balance of older people for diagnosing a balance impairment, which is caused by muscle weakness in stroke patients. Sixty participants were invited to attend the risk assessment that included a clinical test. Four biofeedback testing patterns were tested with the participants. Two machine learning algorithms were selected to experiment using 10-fold cross validation scenario. The results show that Artificial Neuron Network has the best evaluation performance of 86.67%, 80%, and 80% in three out of four biofeedback testing patterns. This demonstrates that the application of static balance measurement together with Wii Fit Balance Board® could be implemented as a tool to replace high-cost force plate systems.
|Number of pages||6|
|Publication status||Published - Oct 2017|
|Event||GWS 2017 - Global Wireless Summit - Cape Town, South Africa|
Duration: 15 Oct 2017 → 18 Oct 2017
|Conference||GWS 2017 - Global Wireless Summit|
|Period||15/10/17 → 18/10/17|