# 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/s\({}^{2}\) 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%.  
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Mobile health (mHealth) methodologies [11; 12] are inexpensive and widely available, and are used for monitoring gait disturbances associated with chronic diseases [13]. Additionally, mHealth is a nice tool for providing real-time assessment and receiving feedback because it works on a smartphone. Real-time assessment and feedback [14] can both detect and prevent further functional deterioration, as well as save affected individuals multiple trips to clinics [15; 16].  
In this study, smartphone sensors collected data on leisurely walking for subsequent analysis using deep learning. Data for six mTBI subjects and one non-TBI subject were collected using a mHealth application that accesses two hardware sensors and one software sensor on smartphone devices that were worn by the subjects. On-board hardware recorded acceleration in m/s2, with the accelerometer recording acceleration in X, Y, and Z directions, and a gyroscope measuring acceleration with respect to rotation around a spatial axis. In addition, nine parameters associated with a rotation vector and rotation vector component along the X, Y, and Z axes were recorded using rotation vector software-based sensors previously installed on the Android devices. The subjects walked about 220-feet round trips. Walking data for the seven subjects were imported into a deep learning model originally built with TensorFlow [17] and Keras [18] in R, and subsequently classified in order to improve accuracy of the deep learning model. Model training with accelerometer data yielded a 50.5% accuracy in classifying subjects' walking. Deep learning model training with Gyroscope data presented an accuracy of 48.3%. The accuracy of the model trained with the rotation vector component along the X, Y, and Z axis data was 77.1%. Model training with the nine parameters of rotation vector was 99.5%. Model training with all data sets yielded a 99.9% accuracy in predicting the classification of the subject's walking.