Fig. 2

The structure of convolutional autoencoders employed for deep clustering of movement behavior images. The encoder network converts the input data into a compressed representation, and the decoder network reconstructs the original input data from the learned compressed representation. The encoder network comprises convolutional layers, and the decoder network comprises deconvolutional layers (or convolutional transpose layers). In the middle lies a fully connected autoencoder, whose embedded layer consists of 32 neurons, creating the latent representation. The network was trained in an end-to-end manner. The clustering layer received the latent representations as input and employed K-means clustering to divide the data into non-overlapping clusters