  The Internet of Things (IoT) connects people, devices, and information
resources, in various domains to improve efficiency. The healthcare domain has
been transformed by the integration of the IoT, leading to the development of
digital healthcare solutions such as health monitoring, emergency detection,
and remote operation. This integration has led to an increase in the health
data collected from a variety of IoT sources. Consequently, advanced
technologies are required to analyze health data, and artificial intelligence
has been employed to extract meaningful insights from the data. Childhood
overweight and obesity have emerged as some of the most serious global public
health challenges, as they can lead to a variety of health-related problems and
the early development of chronic diseases. To address this, a self-adaptive
framework is proposed to prevent childhood obesity by using lifelog data from
IoT environments, with human involvement being an important consideration in
the framework. The framework uses an ensemble-based learning model to predict
obesity using the lifelog data. Empirical experiments using lifelog data from
smartphone applications were conducted to validate the effectiveness of human
involvement and obesity prediction. The results demonstrated the efficiency of
the proposed framework with human involvement in obesity prediction. The
proposed framework can be applied in real-world healthcare services for
childhood obesity.
