Modeling spatiotemporal patterns of gait anomaly with a CNN-LSTM deep neural network

摘要

In this work, we propose an end-to-end deep learning model that uses the skeleton data recorded by Kinect to capture spatiotemporal patterns for gait anomaly recognition. Via considering the entire skeleton, the proposed model captures the relationship between different body joints in locomotion. Unlike the common two-class or one-class approaches in skeleton-based methods, the proposed model considers a multi-class framework. Such a multi-class technique can be easily adapted for a more frequent and less expensive gait assessment outside of motion capture facilities. The proposed deep learning model is trained and evaluated on the publicly available Walking gait dataset and achieves an average accuracy of 90.57% in identifying nine different walking patterns. Through transfer learning, we also evaluate our model on two other publicly available datasets, acquiring an average accuracy of 83.64% on a dataset of three classes and 90.83% on a dataset with six classes of normal/ pathological gait patterns. The results of this work indicate the potential of markerless modalities such as Kinect for designing less costly and more convenient health infrastructures for assisted living. Additionally, an automatic and non-invasive gait assessment can further augment the clinical diagnosis for an extensive list of ailments that cause different types of gait disorders.