Fig. 1

Overview of the proposed FOG detection model architecture. Our proposed FOG detection model comprises two essential blocks: an initial prediction block and a prediction refinement block. The initial prediction block takes the six-dimensional signal of T samples from each of the five IMUs and generates initial predictions with the probabilities of positive (FOG) and negative (non-FOG) classifications for each sample within the input sequence. Consequently, the output sequence is structured as \(T \times 2\) representing the probabilities of the two classes. The prediction refinement block aims to refine the initial predictions. This block takes the initially predicted probabilities of the two classes as input and applies a smoothing process, removing over-segmentations and enhancing the overall prediction quality. The output of this refinement block is a refined prediction, also structured as \(T \times 2\) representing the probabilities of the two classes