Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things.

Sensors (Basel, Switzerland)·2025

Same author

From Network Sensors to Intelligent Systems: A Decade-Long Review of Swarm Robotics Technologies.

Sensors (Basel, Switzerland)·2025

Same author

TwinFedPot: Honeypot Intelligence Distillation into Digital Twin for Persistent Smart Traffic Security.

Sensors (Basel, Switzerland)·2025

Same author

Task-Offloading Optimization Using a Genetic Algorithm in Hybrid Fog Computing for the Internet of Drones.

Sensors (Basel, Switzerland)·2025

Same author

FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications.

Sensors (Basel, Switzerland)·2024

Same author

LLM Multimodal Traffic Accident Forecasting.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning

Mohamed Bahache¹, Abdou El Karim Tahari¹, Jorge Herrera-Tapia²

¹Laboratoire d'Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, Algeria.

Sensors (Basel, Switzerland)

|August 12, 2022

Summary

This study introduces a hybrid machine learning and statistical approach for fault detection in Wireless Body Area Networks (WBANs). The method accurately identifies sensor faults, enhancing remote patient monitoring systems.

Keywords:

WBANs body sensor cloud computing clustering fault detection machine learning

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: Sep 1, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Biomedical Engineering
Computer Science
Signal Processing

Background:

Remote healthcare monitoring is a critical research area, with Wireless Body Area Networks (WBANs) enabling patient supervision via implanted wireless sensors.
These body sensors have limited resources, making them susceptible to faults and damage, necessitating robust fault detection systems.

Purpose of the Study:

To develop an optimized, hybrid fault detection system for WBANs.
The system aims to detect anomalies in sensed data without impacting device resources or functionality.

Main Methods:

A novel hybrid approach combining machine learning and statistical techniques was developed.
The method is designed for resource-constrained WBAN devices.

Main Results:

The proposed solution achieved a fault detection accuracy exceeding 99.62%.
It demonstrated a low mean absolute error of 0.61%.
Performance significantly outperformed existing state-of-the-art solutions.

Conclusions:

The hybrid approach offers an efficient and accurate solution for detecting faults in WBANs.
This technology enhances the reliability of remote patient monitoring systems.
The method is suitable for resource-limited implanted sensor networks.