Next Article in Journal
A n-out-of-n Sharing Digital Image Scheme by Using Color Palette
Previous Article in Journal
Effectiveness Assessment of a Nanocrystalline Sleeve Ferrite Core Compared with Ceramic Cores for Reducing Conducted EMI
Previous Article in Special Issue
A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

An Intelligent Air Quality Sensing System for Open-Skin Wound Monitoring

1
Department of Computer Science, Govt Sadiq College Women University, Bahawalpur 63100, Pakistan
2
Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 801; https://doi.org/10.3390/electronics8070801
Received: 16 May 2019 / Revised: 27 June 2019 / Accepted: 8 July 2019 / Published: 17 July 2019
  |  
PDF [6365 KB, uploaded 17 July 2019]
  |  

Abstract

There are many factors that may have a significant effect on the skin wound healing process. The environment is one of them. Although different previous research woks have highlighted the role of environmental elements such as humidity, temperature, dust, etc., in the process of skin wound healing, there is no predefined method available to identify the favourable or adverse environment conditions that seriously affect (positively or negatively) the skin wound healing process. In the current research work, an IoT-based approach is used to design an AQSS (Air Quality Sensing System) using sensors for the acquisition of real-time environment data, and the SVM (Support Vector Machine) classifier is applied to classify environments into one of the two categories, i.e., “favourable”, and “unfavourable”. The proposed system is also supported with an Android application to provide an easy-to-use interface. The proposed system provides an easy and simple means for patients to evaluate the environmental parameters and monitor their effects in the process of open skin wound healing. View Full-Text
Keywords: IoT; health sensors; machine learning; SVM classifier IoT; health sensors; machine learning; SVM classifier
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Sattar, H.; Bajwa, I.S.; Shafi, U.F. An Intelligent Air Quality Sensing System for Open-Skin Wound Monitoring. Electronics 2019, 8, 801.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top