Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study

• URL: http://arxiv.org/abs/2306.04335v2
• Date: Sat, 10 Aug 2024 13:03:00 GMT
• Title: Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study
• Authors: Mbithe Nzomo, Deshendran Moodley,
• Abstract summary: This study analyses the state of the art in the use of Semantic Web technologies in sensor-based personal health monitoring systems. We critically analyse the extent to which the selected systems address seven key challenges: interoperability, context awareness, situation detection, situation prediction, decision support, explainability, and uncertainty handling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. This study analyses the state of the art in the use of Semantic Web technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 43 systems are selected as representative of the current state of the art. We critically analyse the extent to which the selected systems address seven key challenges: interoperability, context awareness, situation detection, situation prediction, decision support, explainability, and uncertainty handling. We discuss the role and limitations of Semantic Web technologies in managing each challenge. We then conduct a quality assessment of the selected systems based on the data and devices used, system and components development, rigour of evaluation, and accessibility of research outputs. Finally, we propose a reference architecture to provide guidance for the design and development of new systems. This study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research.

Related papers

• Looking back and forward: A retrospective and future directions on Software Engineering for systems-of-systems [0.11470070927586014]
  The textitInternational Workshop on Software Engineering for Systems-of-Systems (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023).
  arXiv  Detail & Related papers  (2024-03-25T13:12:39Z)
• Hair and scalp disease detection using deep learning [0.3958317527488534]
  This paper introduces a pioneering approach in dermatology, presenting a robust method for the detection of hair and scalp diseases. Our methodology relies on Convolutional Neural Networks (CNNs), well-known for their efficacy in image recognition. Our proposed system represents a significant advancement in dermatological diagnostics, offering a non-invasive and highly efficient means of early detection and diagnosis.
  arXiv  Detail & Related papers  (2024-03-09T04:49:40Z)
• A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality [4.522609963399036]
  The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD) This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients conditions.
  arXiv  Detail & Related papers  (2023-09-13T01:01:16Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems [128.63953314853327]
  "Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
  arXiv  Detail & Related papers  (2023-01-18T21:58:54Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey [16.20885642028316]
  Infrastructure-based object detection and tracking systems can enhance the perception capability for connected vehicles. Discussions conducted to point out current opportunities, open problems, and anticipated future trends.
  arXiv  Detail & Related papers  (2022-01-28T00:55:24Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects [3.3336265497547126]
  The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies. This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment.
  arXiv  Detail & Related papers  (2021-06-24T05:10:47Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

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.