With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health.

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UNSTRUCTURED The human walking balance is achieved and maintained by a complex set of human sensorimotor and musculoskeletal systems that control vision, proprioception, vestibular function, muscle contraction, and more. The elements of walking can be used for measurement in healthy and pathological subjects, and is used to diagnose disorders related to the nervous system. This study presents a new approach to measure and analyze the walking balance of human subjects using motion sensors in a smart phone. We developed a mobile health (mHealth) application to collect the coordinate of gyroscope, rotation matrix and coordinate of accelerometer and so on, of which this study used rotation matrix to compare the walking balance among the human subjects. Rotation vector, calculated by rotation matrix, was used to measure the pitch, roll and yaw angles of the human body while walking using the smartphone worn in the middle of waistline (as the center of gravity). Data was collected from three subjects including two healthy individuals (one female and one male) and one male with back pain caused from an over-strenuous workout. The male experiencing back pain injured his back during exercise but does not have any issues walking. The participants were wearing the smartphone in the middle of waistline while walking. The male with back pain exhibited a diminished level of walking balance with a wider range of variation than the other subjects. While this study tested the mHealth application with only three subjects, it showed that the new method effectively monitored their postural balance while walking. Our mHealth system can be implemented in mobile devices for supporting clinical assessment and diagnosis by estimating postural balance while walking.

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%.