Exploring unsupervised feature extraction of IMU-based gait data in stroke rehabilitation using a variational autoencoder
Fig 5
Distribution of the latent features (L0-L11) and gait speed (z-normalised).
The results of the healthy participants are colored in orange, the results for people after stroke are colored in blue. The * indicates a variable with a high-excellent reliability. The # indicates a significant difference between healthy participants and people after stroke. The height of the distributions on the y-axis indicates the range of the latent variable. The width of the distribution on the x-axis indicates the height of the peak. Since the latent variables are computed with a VAE, the distributions of the stroke group are roughly normally distributed around 0 and are roughly normally distributed. Visual inspection indicates some differences between the healthy and stroke group. First, for the healthy participants, L0 appears to follow a bi-modal distribution. Second, L1 demonstrates a peak at another height than the peak of the stroke group.