Abstract
Threshold-based fall detection has been widely adopted in conventional fall detection systems. In this paper, we argue that a fixed threshold is not flexible enough for different people. By exploiting the personalised and adaptive threshold, we propose a novel threshold extraction model, which meets being adaptive to detect a fall, while only taking consideration of data from activity of daily living ADL. We believe this is a solid step toward improving the performance of the threshold-based fall detection solution. Furthermore, we incorporate the proposed idea into Chameleon. To evaluate the performance of this threshold extraction model, we compared Chameleon with advanced magnitude detection AMD and fixed and tracking fall detection FTFD. The results show Chameleon has an accuracy of 96.83% when detecting falls, which is 1.67% higher than FTFD and 2.67% higher than AMD. Meanwhile, the sensitivity and the specificity of Chameleon are also higher than the other two algorithms.
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