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

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.

[1]  A. Chan,et al.  Control and management of hospital indoor air quality. , 2006, Medical science monitor : international medical journal of experimental and clinical research.

[2]  T. K. Hunt,et al.  Physiology of wound healing. , 2000, Advances in skin & wound care.

[3]  S. Sathiya Keerthi,et al.  Building Support Vector Machines with Reduced Classifier Complexity , 2006, J. Mach. Learn. Res..

[4]  K. Harding,et al.  Bacteria and wound healing , 2004, Current opinion in infectious diseases.

[5]  M. Dyson,et al.  Comparison of the effects of moist and dry conditions on dermal repair. , 1988, The Journal of investigative dermatology.

[6]  L. DiPietro,et al.  Factors Affecting Wound Healing , 2010, Journal of dental research.

[7]  Burak Kantarci,et al.  On the Feasibility of Deep Learning in Sensor Network Intrusion Detection , 2019, IEEE Networking Letters.

[8]  Gian Carlo Cardinali,et al.  An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications , 2004 .

[9]  Maradugu Anil Kumar,et al.  Android based health care monitoring system , 2015, 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[10]  Riyanarto Sarno,et al.  Estimating Gas Concentration using Artificial Neural Network for Electronic Nose , 2017 .

[11]  Vikramaditya R. Jakkula,et al.  Tutorial on Support Vector Machine ( SVM ) , 2011 .

[12]  Tiberiu Catalina,et al.  Indoor Environmental Quality Experimental Studies in an Energy-efficient Building. Case study: EFdeN Project , 2017 .

[13]  Bengisu Tulu,et al.  The smartphone as a medical device: Assessing enablers, benefits and challenges , 2013, IOT 2013.

[14]  Xiaochuan Pan,et al.  Pollution and skin: from epidemiological and mechanistic studies to clinical implications. , 2014, Journal of dermatological science.

[15]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[16]  Yaser Jararweh,et al.  An intrusion detection system for connected vehicles in smart cities , 2019, Ad Hoc Networks.

[17]  Kavi Kumar Khedo,et al.  A Wireless Sensor Network Air Pollution Monitoring System , 2010, ArXiv.

[18]  H. T. Mouftah,et al.  A Generalized Framework for Quality of Experience (QoE)-Based Provisioning in a Vehicular Cloud , 2015, 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB).

[19]  Robert Koprowski,et al.  Machine learning, medical diagnosis, and biomedical engineering research - commentary , 2014, BioMedical Engineering OnLine.

[20]  Nobuo Funabiki,et al.  Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system , 2017, Int. J. Space Based Situated Comput..

[21]  Babak Ziaie,et al.  A low-cost flexible pH sensor array for wound assessment , 2016 .

[22]  G. Troster,et al.  A textile integrated sensor system for monitoring humidity and temperature , 2011, 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference.

[23]  Liviu Iftode,et al.  Real-time air quality monitoring through mobile sensing in metropolitan areas , 2013, UrbComp '13.

[24]  Mohamed Adel Serhani,et al.  Novel Cloud and SOA-Based Framework for E-Health Monitoring Using Wireless Biosensors , 2014, IEEE Journal of Biomedical and Health Informatics.

[25]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[26]  C Torrance,et al.  The physiology of wound healing. , 1986, Nursing.

[27]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Eric Paulos,et al.  InAir: sharing indoor air quality measurements and visualizations , 2010, CHI.

[29]  Thomas K. Hunt,et al.  Accelerated Healing of Full-Thickness Skin Wounds in a Wet Environment , 2000 .

[30]  Clelia Dispenza,et al.  RFID epidermal sensor including hydrogel membranes for wound monitoring and healing , 2015, 2015 IEEE International Conference on RFID (RFID).

[31]  Shekhar Bhansali,et al.  Continuous Monitoring of Wound Healing Using a Wearable Enzymatic Uric Acid Biosensor , 2018 .