A self-adaptive framework for child healthcare in IoT environment using AI-based prediction

초록

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. The healthcare domain has been transformed by the integration of the Internet of Things (IoT), leading to the development of digital healthcare solutions. 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 (AI) has been employed to extract meaningful insights from the data. Moreover, these technologies can be effectively applied in healthcare to address childhood overweight and obesity. A self-adaptive framework is proposed to manage childhood weight using lifelog data from IoT environments. An ensemble-based learning model is applied to predict weight using the lifelog data. A smartphone application providing real-world services was developed, and lifelog data were collected from a cohort of 362 children aged 104 to 152 months. Reasonable results were obtained, indicating that the proposed ensemble model can predict childhood weight change using lifelog data with an accuracy of 0.9711 and an F1-score 0.9725. Also, the results indicated that appropriate rewards can encourage human involvement and positively influence data quality.The results demonstrated the efficiency of the proposed framework with human involvement in weight prediction. The experimental results demonstrate the efficiency of the proposed framework and its potential application in healthcare services to enhance children's health.

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