https://terra-docs.s3.us-east-2.amazonaws.com/IJHSR/Articles/volume6-issue8/IJHSR_2024_68_32.pdf

The utilization of smart wearable devices in healthcare environments is expected to increase as digital technology advances.Smart wearables and the data they provide have become prevalent in medical care applications.However, evaluating clinical applications is challenging due to the need to accumulate more information and the limitations of technical approaches.By applying measures of the established digital health platform that incorporates smart wearables for adolescent brain cognitive development (ABCD), we investigated the optimization of ordinary physical activity as determined by the individual participants.It aims to provide reliable leverage for physical metabolic classification.First, the adolescent physical activity datasets are evaluated statistically for this analysis.Then, multi-dimensional Bayesian optimization (BO) is applied to extract optimized MET (metabolic equivalent of task) values to categorize physical development behavior.The result shows that the practical MET level is overestimated, and the real value is lower than expected.