2020/01 Recurrent Neural Network for Gait Pathology Detection


“This work presents a pathology detection system on the lower train. For this, a database of healthy subjects has been captured. Due to the nonexistence of pathological gait databases, pathology walks have been simulated. The users used sole padding in order to simulate clubfoot walk. The database consists of acceleration, angular acceleration, magnetic field signals and the angles between the joints. The algorithm extracts fragments of the signals which are used to train a recurrent neural network (RNN). To optimize the results, hand-tuning method was used to modify the hyperparameters. Using the best configuration, we have a 97% accuracy training with 90% of the database. Although, if we train with only 50% of the data the accuracy reaches at 91%. The results obtained show the solution feasibility, although further research should be done using real lower train pathologies”