Deep Echo State Networks for Functional Ambulation Categories Estimation

In this work, we introduce a novel application for the automatic estimation of Functional Ambulatory Category (FAC) based on deep Echo State Networks (ESNs). FAC is a clinical scale for assessing the gait ability used in post-stroke rehabilitation and, in general, for disease monitoring. In this application, the estimation is performed automatically by analyzing signals gathered from wearable sensors (located on both tibiae, pelvis, trunk and head) during the execution of a walking test. This is performed by analysing the whole time-series through the DeepESN model without preprocessing. The experimental results show that the use of a deep recurrent neural network allows the model to exploit the richness contained in the whole raw temporal signal improving the performance w.r.t. the shallow recurrent model. Overall, our approach obtained 0.37 of mean absolute error with a maximum error of 0.78 resulting very accurate in the classification of the gait ability through the estimation of the FAC value. Considering the experimental results obtained, the proposed approach represents a good baseline for medical applications based on the automatic estimation of the FAC scale.