Figure 3.

Architecture of a MLP model with H hidden layers for sensor-based HAR. Input data from the different sensor channels are first flattened into a $(T\times S)$-dimensional vector and then fed to the hidden layers. All layers are fully-connected. The numbers in parenthesis indicate the number of neurons per layer. T, S and N designate the time length of the input data, the number of sensor channels, and the number of classes, respectively.