Development of a Sensor-Based Heartbeat and Body Temperature Monitoring System for Remote Chronic Patients

Wireless sensor networks are known for their monitoring and tracking application-specific operations. These operations diversely demand improvement in existing strategies and their parameters. One key parameter is energy usage during operations. Energy plays a vital role in each application, as the wireless sensor networks lack battery lifetime and energy resources. So, there is a need for an optimized and efficient routing method with regard to energy consumption in wireless sensor networks. For multi-hop routing, the genetic algorithm serves as a robust algorithm with diverse optimized routing plans to improve the lifespan for large-scale wireless sensor networks. In this paper, the genetic algorithm provides the optimized routes for data operations and improves the lifetime of wireless sensor networks by saving energy. The performance of the genetic algorithm is compared with the TEEN algorithm.

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks

Rajab

2021

“…Across the world, 251 million people suffer from COPD [5]. COPD is one of the primary contributors to worldwide mortality rates [6][7], accounting for approximately 90% of deaths in low-and middle-income countries [8], and it is expected that 4.5 million people worldwide will have been affected by COPD and its related conditions by 2030 [9]. People with COPD have an increased susceptibility to heart disease, lung cancer, and a variety of other health problems [10].…”

Section: Introductionmentioning

confidence: 99%

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Siddiqui,

Latif,

Farooq

et al. 2024

Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care.

“…The advantage of the proposed model is that it uses GPS coordinates and population density values towards identifying the most crowded areas. Authors in [13] proposed a system prototype for monitoring the heartbeat rate and body temperature of chronic patients using sensors. The monitored data are sent to a cloud database in real-time.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Medical TeleMonitoring System based on IoT

Hamdani

Youcefi²,

Rabehi³

et al. 2022

With the advancement of technology, healthcare and monitoring systems face significant issues. Internet of Things (IoT) in healthcare enables real-time health monitoring at a low cost. This paper aims to provide a medical telemonitoring system based on IoT for healthcare applications for doctors and paramedical staff. The system is made up of many sensors that can capture electrocardiograms (ECGs) in real-time and measure the temperature of the human body. The designed circuit is implemented and the obtained results are analyzed and discussed.