Figure 6.

Schematic of deep transfer learning approach. ${\mathcal{D}}_S$ refers to input data from a source domain, in this case a HAR dataset, to learn a task ${\mathcal{T}}_S$, which is represented by the label space ${\mathcal{Y}}_S$ (the HAR activity classes). ${\mathcal{D}}_T$ refers to the target domain, in this case the FLOODLIGHT data, where ${\mathcal{Y}}_T$ are the disease classification outputs of HC, PwMSmild or PwMSmod for target task ${\mathcal{T}}_T$. During transfer learning, a model’s parameters and learned weights, $f(·)$ of ${\mathcal{D}}_S$, are then used to initialise and train a model on target domain ${\mathcal{D}}_T$ and task ${\mathcal{T}}_T$. Transfer learning is then performed by transferring the source model’s layers (where these weights and parameters are “frozen”) to subsequently re-train a new model (i.e. fine-tune) using ${\mathcal{D}}_T$ data for the new target task, ${\mathcal{T}}_T$. Downstream layers in the network are fine-tuned towards this new target task decision ${\mathcal{Y}}_S$.