Normal and Pathological Gait Classification LSTM model

Publication date: Available online 11 January 2019Source: Artificial Intelligence in MedicineAuthor(s): Margarita Khokhlova, Cyrille Migniot, Alexey Morozov, Olga Sushkova, Albert DipandaAbstractComputer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis. Second, we adapt to use the skeleton joint orientation data to calculate kinematic gait parameters to match golden-standard MOCAP systems. We propose a new set of features based on 3D low-limbs flexion dynamics to to analyze the symmetry of a gait. Third, we design a Long-Short Term Memory (LSTM) ensemble model to create an unsupervised gait classification tool. The results show that joint orientation data provided by Kinect can be successfully used in an inexpensive clinical gait monitoring system, with the results moderately better than reported state-of-the-art for three normal/pathological gait classes.

Source: Artificial Intelligence in Medicine - January 11, 2019 Category: Bioinformatics Source Type: research