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

• URL: http://arxiv.org/abs/2308.15944v1
• Date: Wed, 30 Aug 2023 10:52:00 GMT
• Title: WUDI: A Human Involved Self-Adaptive Framework to Prevent Childhood Obesity in Internet of Things Environment
• Authors: Euijong Lee, Jaemin Jung, Gee-Myung Moon, Seong-Whan Lee, and Ji-Hoon Jeong
• Abstract summary: A self-adaptive framework is proposed to prevent childhood obesity by using lifelog data from IoT environments. The framework uses an ensemble-based learning model to predict obesity using the lifelog data. The proposed framework can be applied in real-world healthcare services for childhood obesity.
• Score: 25.046936884407017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems [2.8351008282227266]
  Mobile health has the potential to revolutionize health care delivery and patient engagement. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations can significantly improve the impact of digital tools on health system outcomes.
  arXiv  Detail & Related papers  (2024-09-24T13:52:15Z)
• Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery [1.03590082373586]
  The review highlights various types of mHealth applications used for remote monitoring, such as telemedicine platforms, mobile apps for chronic disease management, and wearable devices. The benefits of these applications include improved patient outcomes, increased access to healthcare, reduced healthcare costs, and addressing healthcare disparities. However, challenges and limitations, such as privacy and security concerns, lack of technical infrastructure, regulatory is-sues, data accuracy, user adherence, and the digital divide, need to be addressed.
  arXiv  Detail & Related papers  (2024-08-26T11:32:43Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems [69.41229290253605]
  Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data. We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
  arXiv  Detail & Related papers  (2024-01-19T16:26:35Z)
• DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework [27.82565790353953]
  Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction.
  arXiv  Detail & Related papers  (2023-08-28T15:40:31Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• A Comprehensive Picture of Factors Affecting User Willingness to Use Mobile Health Applications [62.60524178293434]
  The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps. Users' digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information. Users' demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect.
  arXiv  Detail & Related papers  (2023-05-10T08:11:21Z)
• An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model [0.966840768820136]
  This study seeks to expand our existing Urban Population Health Observatory (UPHO) system. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. The application of UPHO could help reduce health disparities and improve urban population health.
  arXiv  Detail & Related papers  (2022-07-26T15:59:22Z)
• Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
  This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
  arXiv  Detail & Related papers  (2021-08-06T05:46:01Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)
• Collecting big behavioral data for measuring behavior against obesity [4.879286072217533]
  We present a system for extracting and collecting behavioral information on the individual-level objectively and automatically. The behavioral information is related to physical activity, types of visited places, and transportation mode used between them. The system has been developed and integrated in the context of the EU-funded BigO project that aims at preventing obesity in young populations.
  arXiv  Detail & Related papers  (2020-05-11T08:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.