A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/cin/volume-2017/1512670/figures/1512670.fig.005a.svgz" name="1512670.fig.005a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>UBM training scheme</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/cin/volume-2017/1512670/figures/1512670.fig.005b.svgz" name="1512670.fig.005b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>The GMS extraction procedure</td></tr></table>

<div>Training of the Universal Background Model from MFCCs (a) and extraction of Gaussian Mean Supervectors (b).</div>

Computational Intelligence and Neuroscience

fig5

Figure 5

Figure 5: A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features 