## 5 Conclusions and Future Work  
The major contribution of our study is to support individuals who retain healthy weights within the range by detecting obesity using the mHealth application in smartphones. The nine vectors of rotation in the center of the human body were used for distinguishing a normal and an obese group, using a deep learning algorithm. The rotation vectors in the center of the human body of 244 students were measured in the PE room by the mHealth application that we developed. The student participants walked straight forward 78 feet, and returned back to their original place, walking approximately 157 feet. The data of 170 students among 244 students were used for training and testing the feedforward neural network and the convolutional neural network. On the basis of the models built based on testing the student samples, the feedforward neural network successfully distinguished the nonobese from the obese group at 90.5%, and the accuracy of the convolutional neural network was 79%. Additionally, according to the testing of the models using the students with a BMI % of the 97 th percentile, the students in the group with a BMI% \(\geq\) 97 th percentile show different walking movements than those students with a BMI% \(<\) 97 th percentile. Obesity in adolescents has a higher likelihood to persist, even into their adult lives [43]. To disconnect such a negative loop, we highlight the importance of offering adolescents' real-time feedback of their current state of high BMI for self-cognition. By building a platform using an easily installed mHealth app and deep learning models, we may provide real-time feedback to the adolescents who present the walking patterns of higher BMI groups, without incurring special medical expenses. Moreover, with the help of the mHealth app, which detects walking patterns, the continuous real-time notification from the smartphones can support the customers in monitoring their BMI actively. The present study has several limitations. The mHealth application and the data analysis were only verified with a small group. Nonetheless, as suggested by our results, the proposed mHealth can effectively capture differences in postural control during walking in healthy individuals vs. individuals with obesity. In further research, we will conduct feasibility and efficacy testing with larger pools of individuals with reduced physical mobility. In addition, any variables that may affect postural balance during walking, such as body habitus or the level of daily physical activities, will be considered in the data collection and analysis. We will also consider evaluating the impact of the application at the health system level, using outcomes such as health care utilization and medication use. For the evaluation of the health system level, we consider applying the research in polycystic ovary syndrome, which affects the lifestyles of women [44]. For future work, we plan to collect more student samples to increase the accuracy of the models and, especially, for the convolutional neural network. Moreover, the vectors  
Figure 5: Losses and Accuracies with 97% BMI dataset. of rotation are very different among go-straight-forward walking, return-straight-forward walking, and curve walking. However, we used the three different walking rotation data in the research to increase the number of the input data. The current research participants walked about 78 feet straightforward, there was a turning section, and they then walked an additional 78 feet to return to their starting point, which can be divided into three areas. In the future, by dividing the entire 157 feet into three sections, we will further increase the accuracy of the four models. Conceptualization, S.L. and E.J. ; Data curation, J.J.M. ; Formal analysis, H.L. ; Investigation, S.L., E.H. and Y.K. ; Methodology, F.D. and K.L. ; Software, J.J.M. ; Supervision, K.L. ; Writing--original draft, S.L. ; Writing--review & editing, K.L. All authors have read and agreed to the published version of the manuscript.