Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons

Wambui, Nagumi

doi:10.53759/0088/jbsha202202002

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every DGAST round consists of four phases: data aggregation, data accumulation, selective transfer, and adjusting the frequency of samples acquired for SN. The suggested model saves electricity and increases the lifetime of sensor networks; however, it requires complex computations and a lot of memory [20].…”

Section: Fig 1 Network Model Examplementioning

confidence: 99%

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

2023

JMC

View full text Add to dashboard Cite

Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).

show abstract

Section: Fig 1 Network Model Examplementioning

confidence: 99%

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

2023

JMC

View full text Add to dashboard Cite

show abstract

Multi-Scale Object Detection and Classification using Machine Learning and Image Processing

Yuvaraj,

Rajput,

Suganyadevi

et al. 2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

View full text Add to dashboard Cite

Navigating Through Innovation in Elderly’s Health: A Scoping Review of Digital Health Interventions

Hirmas-Adauy,

Castillo-Laborde,

Awad

et al. 2024

Public Health Rev

View full text Add to dashboard Cite

ObjectivesComprehensively map and summarize digital health initiatives for the elderly and caregivers.MethodsScoping review between April and May 2022 based on Joanna Briggs methodology. Databases used included PubMed, Cochrane Library, CINAHL Plus, and Web of Science, along with grey literature and hand searches. Two reviewers independently conducted screening and eligibility phases, with a third resolving disagreements. Data were thematically analyzed.ResultsThe review included 421 documents. Most documents were published between 2013 and 2022, with a recent increase. Most studies, originating from high-income countries, focused on home applications and were mainly in the testing and validation stages. Telephones and computers were the predominant devices. Health objectives included monitoring, prevention, and treatment, with interventions utilizing directed communication and personal health monitoring for individuals, and telemedicine and decision support for healthcare providers.ConclusionIncreasing integration of technology in older adults’ lives, along with their increasing proficiency, is driving a significant rise in digital health interventions. Despite this growth, further research in middle- and low-income countries, for caregivers and evaluating effectiveness and feasibility of these technological interventions is needed.

show abstract

Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons

Cited by 3 publications

References 14 publications

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

Multi-Scale Object Detection and Classification using Machine Learning and Image Processing

Navigating Through Innovation in Elderly’s Health: A Scoping Review of Digital Health Interventions

Contact Info

Product

Resources

About