Abstract
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.
Highlights
Since 1960, obesity has continually increased in the United States of America
According to the National Heart, Lung, and Blood Institute, obesity has been acknowledged as a serious medical concern that is a major contributor to potential health risks, such as heart disease, type 2 diabetes, high blood pressure, and so on [1,2]
According to the reports of the U.S Department of Health and Human Services, more than 300,000 people died in one year because of the obesity epidemic in the United States [3,4]
Summary
Since 1960, obesity has continually increased in the United States of America. According to the National Heart, Lung, and Blood Institute, obesity has been acknowledged as a serious medical concern that is a major contributor to potential health risks, such as heart disease, type 2 diabetes (high blood sugar), high blood pressure, and so on [1,2]. According to the reports of the U.S Department of Health and Human Services, more than 300,000 people died in one year because of the obesity epidemic in the United States [3,4]. The walking balance can become less stable if an individual has experienced a stroke [20], or lower limb or back injury [21], because of the fragile biomechanical structures in the sensorimotor and musculoskeletal systems that influence how the human body moves while walking [22]. By self-recognizing their obesity level status in real time using the mHealth application and the deep learning model, students can become alert and encouraged to make self-care efforts. The mHealth application [28] was developed to measure and record rotational data in real time using an Android smartphone’s motion sensors [29]. The data is saved to an SQLite database and (comma-separated value) CSV files stored on the smartphone
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