Detection of Walking Features Using Mobile Health and Deep Learning

Abstract

This study identifies seven human subjects’ walking features by training a deep learning model with sensor data. Using the proposed Mobile Health Application developed for collecting sensor data from an Android device, we collected data from human subjects with a history of mild traumatic brain injury. The sensors measure acceleration in m/s2 with respect to: the X, Y, and Z directions using an accelerometer, the rate of rotation around a spatial axis with a gyroscope, and nine parameters of a rotation vector with rotation vector components along the X, Y, Z axes using a rotation vector software-based sensor. We made a deep learning model using Tensorflow and Keras to identify the walking features of the seven subjects. The data are classified into the following categories: Accelerometer (X, Y, Z); Gyroscope (X, Y, Z); Rotation (X, Y, Z); Rotation vector (nine parameters); and a combination of the preceding categories. Each dataset was then used for training and testing the accuracy of the deep learning model. According to the Keras evaluation function, the deep learning model trained with Rotation vector data shows 99.5% accuracy for classifying walking characteristics of subjects. In addition, the ability of the model to accurately classify the characteristics of subjects’ walking with all datasets combined is 99.9%.