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.

翻译：物联网（IoT）通过连接人、设备及信息资源，在多个领域内提升效率。医疗健康领域因物联网的融合而发生变革，催生了健康监测、紧急检测及远程操作等数字化医疗解决方案。这一整合使得来自多种物联网来源的健康数据量激增，因而需要先进技术来分析这些数据，人工智能被用于从数据中提取有意义的洞察。儿童超重与肥胖已成为最为严峻的全球公共卫生挑战之一，因为它们可导致多种健康问题及慢性疾病的早期发展。为应对此问题，本文提出一种自适应性框架，通过利用物联网环境中的生活日志数据预防儿童肥胖，其中人为介入是该框架的重要考量。该框架采用基于集成学习的模型，利用生活日志数据预测肥胖。通过智能手机应用的生活日志数据进行了实证实验，以验证人为介入的有效性及肥胖预测效果。结果表明，所提出的框架在人为介入下对肥胖预测具有高效性。该框架可应用于现实世界的儿童肥胖医疗保健服务。