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The Internet of Things (IoT) connects people, devices, and informationresources, in various domains to improve efficiency. The healthcare domain hasbeen transformed by the integration of the IoT, leading to the development ofdigital healthcare solutions such as health monitoring, emergency detection,and remote operation. This integration has led to an increase in the healthdata collected from a variety of IoT sources. Consequently, advancedtechnologies are required to analyze health data, and artificial intelligencehas been employed to extract meaningful insights from the data. Childhoodoverweight and obesity have emerged as some of the most serious global publichealth challenges, as they can lead to a variety of health-related problems andthe early development of chronic diseases. To address this, a self-adaptiveframework is proposed to prevent childhood obesity by using lifelog data fromIoT environments, with human involvement being an important consideration inthe framework. The framework uses an ensemble-based learning model to predictobesity using the lifelog data. Empirical experiments using lifelog data fromsmartphone applications were conducted to validate the effectiveness of humaninvolvement and obesity prediction. The results demonstrated the efficiency ofthe proposed framework with human involvement in obesity prediction. Theproposed framework can be applied in real-world healthcare services forchildhood obesity.

WUDI: A Human Involved Self-Adaptive Framework to Prevent Childhood Obesity in Internet of Things Environment