Figure 2.

HC epoch: Panel plot illustrating example performance of a typical HC subject (true negative) which can be visually interpreted using LRP decomposition and CWT frequency analysis. (HC, T25FW: 4.8 ± 0.35) The top row represents a 3-axis accelerometer trace captured from a smartphone over 15.4 seconds, which corresponds to 12 epochs of length 128 samples (or 2.56 [s]) with a 50% overlap. The magnitude $(\Vert \mathit{a}\Vert$) of the 3-axis signal is highlighted in bold. The second row depicts the top view of the CWT scalogram of $\Vert \mathit{a}\Vert$, which is the absolute value of the CWT as a function of time and frequency. The final row depicts the output disease classification probabilities (${\mathcal{T}}_T$). The shaded grey area represents an example epoch (n=128 samples, or 2.56 [s]) within the acceleration trace, which is examined in the larger subplot through the decomposition of DCNN input relevance values ($R_i$) using Layer-Wise Relevance Propagation (LRP). Red and hot colours identify input segments denoting positive relevance ($R_i>0$) indicating $f(\mathit{x})>0$ (i.e. MS). Blue and cold hues are negative relevance values ($R_i<0$) indicating $f(\mathit{x})<0$ (i.e. HC), while black represents ($R_i\approx 0$) inputs which have little or no influence to the DCNNs decision. LRP values are overlaid upon the accelerometer signal, where the bottom panel represents the LRP activations per input (i.e. ${\mathit{a}}_x,{\mathit{a}}_y,{\mathit{a}}_z,\Vert \mathit{a}\Vert$).