Fig. 6

Detailed model architecture of the FOG detection model. Our proposed FOG detection model comprises two essential blocks: an initial prediction block and a prediction refinement block. The initial prediction block utilizes the TCN proposed by Pavllo et al. [52], featuring five temporal convolution layers with valid convolutions. This TCN transforms the input sequence (padded with 121 samples on both sides) of shape \((T+242)\times 30\) into an output of shape \(T\times 2\). The prediction refinement block, leveraging a multi-stage TCN architecture proposed by Farha and Gall [37], aims to refine the initial predictions. The multi-stage TCN comprises four stages of ResNet-style TCN, each containing eight temporal convolution layers with the same convolutions. The output of this refinement block is a refined prediction, also structured as \(T\times 2\), representing the probabilities of the two classes