Classification of Sprain and Non-sprain Motion using Deep Learning Neural Networks for Ankle Sprain Prevention

Abstract

A smart wearable ankle sprain prevention device would require an intelligent monitoring system that can classify data from the sensors as sprain or non-sprain motion. This paper aims to explore Deep Neural Network method, specifically the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) for classifying sprain and non-sprain motion. A study is conducted on 11 participants to record sprain and non-sprain motions, which are used to train and test the LSTM-FCN model and previously used Support Vector Machine (SVM) model. It has been demonstrated that the LSTM-FCN model is more accurate at classifying sprain and non-sprain motion. The LSTM-FCN also proved to be more useful as its architecture allows for the Class Activation Mapping (CAM) method to be employed. The CAM method allows for the identification of temporal regions of the time series that contribute most or least to the classification decision of the LSTMFCN. Visualizing the regions of high or low contribution makes it easy to see patterns in the data correlation with sprain motion and better understand why certain non-sprain data can be misclassified as sprain motion. Overall, LSTM-FCN is found to be a viable method for the classification of sprain and non-sprain motion.