Figure 2
(a) A schematic of a one-layer RNN within the PHTNet with the unfolded version demonstrated on the right-hand side, which clearly shows the processing pipeline for different time instances. For the schematic on the left-hand side, it should be noted that the branch denoted by the weight W also applies one sample delay in time. (b) A gated recurrent unit (GRU) which is employed in PHTNet as the recurrent cell and is equipped with reset gate (r) and update gate (z). (c) The architecture of PHTNet, which is a 4-layer deep bidirectional recurrent neural network. \(\overleftarrow{h}\) defines the backward cells for offline tremor elimination, and \(\overrightarrow{h}\) defines the forward cell of the network for online tremor estimation/prediction. As shown in the diagram, the forward path is completely distinct from the backward path and their outputs are not merged into a single output sequence. The red gradient in the output blocks represents the degree of error in the extracted voluntary component. The high intensity of the red color symbolizes a high degree of error and the opposite mimics lower error rates. (d) The overall workflow of the proposed framework. Note that the voluntary component is recalculated for the next time instance and then is compared with the output of the network. This strategy is taken to enable the network with predictive features.
