EAI Endorsed Transactions on Pervasive Health and Technology

Issue 27, 2021

Editor(s)-in-Chief: Manik Sharma

Articles
Information

m-health services for COVID-19 afflicted and infected victims
Appears in:
phat 21(27): e1

Author:
Manik Sharma

Abstract:
more »
Fusion of Attentional and Traditional Convolutional Networks for Facial Expression Recognition
Appears in:
phat 21(27): e2

Authors:
Tin Trung Nguyen, Thai Hoang Le

Abstract:
INTRODUCTION: The facial expression classification problem has been performed by many researchers. However, it is still a difficult problem to effectively classify facial expressions in highly challenging datasets. In recent years, the self-weighted Squeeze-and-Excitation block …INTRODUCTION: The facial expression classification problem has been performed by many researchers. However, it is still a difficult problem to effectively classify facial expressions in highly challenging datasets. In recent years, the self-weighted Squeeze-and-Excitation block (SE-block) technique has evaluated the importance of each feature map in the Convolutional Neural Networks (CNNs) model, corresponding to the output of the Convolution layer, that has shown high efficiency in many practical applications.

OBJECTIVES: In this paper, with the aim of balancing speed and accuracy for the problem of facial expression classification, we proposed two novel model architectures to solve these problems.

METHODS: Two models proposed in this paper is: (1) a SqueezeNet model combined with a Squeeze-and- Excitation block, (2) SqueezeNet with Complex Bypass combined with a Squeeze-and-Excitation block. These models will have experimented with complex facial expression datasets. Furthermore, the ensemble learning method has also been evidenced to be effective in combining models. Therefore, in order to improve the efficiency of facial expression classification, and aim to compare with the state-of-the-art methods, we use more of the Inception-Resnet V1 model (3). Next, we combine three models (1),(2), and (3) for the classification of facial expressions.

RESULTS: The proposed model gives out high accuracy for datasets: namely, with The Extended Cohn-Kanade (CK+) dataset, there are seven basic types of emotions, reaching 99.10 % (using the last 3 frames), 94.20% for the Oulu-CASIA dataset (from 7th frame) with six basic types of emotions, 74.89% for FER2013.

CONCLUSION: Experimental results on highly challenging data sets (The Extended Cohn-Kanade, FER2013, Oulu-CASIA) show the effectiveness of the technique of combining three models and two proposed models.
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Review on the Role of Macular Edema in Retinopathy, Blindness and Automated Diagnosis Methods
Appears in:
phat 21(27): e3

Authors:
M. J. Barman, D. Deb, M. K. Hassan, B. Choudhury

Abstract:
INTRODUCTION: Macular edema is not a disease itself, but, a very common condition in most of the retinal diseases, such as diabetic retinopathy, retinal vein occlusion, hypertensive retinopathy, age-related macular edema, etc. and post-ocular surgery. OBJECTIVES: We have discussed how macular e…INTRODUCTION: Macular edema is not a disease itself, but, a very common condition in most of the retinal diseases, such as diabetic retinopathy, retinal vein occlusion, hypertensive retinopathy, age-related macular edema, etc. and post-ocular surgery.

OBJECTIVES: We have discussed how macular edema plays an important role in blindness in case of various retinal blood vascular diseases and post-ophthalmic surgery. We have analyzed vast state-of-the-art methods for retinal abnormality detection.

METHODS: The proposed method uses a semi-automated macula segmentation approach and Local Binary Pattern features to train k-Nearest Neighbor classifier and performs binary classification.

RESULTS: We have achieved 80% accuracy and 90% sensitivity in classifying normal and abnormal retina.

CONCLUSION: We justified the notion that it will be beneficial to have a method that can analyse the macula region and alert if there is any abnormality near that region to prevent vision loss.
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Do information and communication technologies influence skills, competencies, health and well-being of teenagers? Current evidence
Appears in:
phat 21(27): e4

Authors:
Willone Lim, Bee Theng Lau, Caslon Chua, Fakir M Amirul Islam

Abstract:
INTRODUCTION: Information and Communication Technologies are deemed to be a significant influence on teenagers’ development. It is important for studies to be conducted to assess the underlying influence of ICT on the teenager age group. OBJECTIVES: The aim of this paper is to …INTRODUCTION: Information and Communication Technologies are deemed to be a significant influence on teenagers’ development. It is important for studies to be conducted to assess the underlying influence of ICT on the teenager age group.

OBJECTIVES: The aim of this paper is to review whether ICT influences teenagers’ skills, competencies, health and well-being based on previous studies.

METHODS: Articles from 2015 to 2020 were selected for analysis to identify the influences on teenagers.

RESULTS: Computer and mobile devices offer opportunities to improve literacy in digital skills and competencies. It is also evident that teenagers are prone to risks of getting musculoskeletal symptoms as a result of excessive use of such devices, and it also leads to unforeseen illnesses such as depression in the case of teenager’s well-being.

CONCLUSION: Adopting ICT may have beneficial implications for teenagers, but it can pose more adverse effects on their development when used excessively.
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Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning
Appears in:
phat 21(27): e5

Authors:
Sanjeev Dhawan, Kulvinder Singh, Mamta Arora

Abstract:
INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depe…INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images.

OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3).

METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images.

RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset.

CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.
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A Review of Medical Image Segmentation Algorithms
Appears in:
phat 21(27): e6

Authors:
K.K.D. Ramesh, G. Kiran Kumar, K. Swapna, Debabrata Datta, S. Suman Rajest

Abstract:
INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this proces…INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM.

OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation.

METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM).

CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.
more »

Scope

EAI Endorsed Transactions on Pervasive Health and Technology is an open access, peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining and knowledge management, IT applications to needs of patients, disease prevention and awareness, electronic and mobile health platforms including design and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency. Authors are not charged for article submission and processing.

Topics

Knowledge Representation and Reasoning

Physiological models for interpreting medical sensor data
Sensing/Actuating Technologies and Pervasive Computing
Medicine, Nursing, and Allied Health Professions
Human-Computer Interaction and Computer Supported Cooperative Work
Hardware …

Knowledge Representation and Reasoning

Physiological models for interpreting medical sensor data
Sensing/Actuating Technologies and Pervasive Computing
Medicine, Nursing, and Allied Health Professions
Human-Computer Interaction and Computer Supported Cooperative Work
Hardware and Software Infrastructures
Activity recognition and fall detection
User modelling and personalization
Modelling of Pervasive Healthcare environments
Sensor-based decision support systems
Design and evaluation of patient and ambient-related sensors
Wearable and implantable sensor integration
Data fusion in pervasive healthcare environments
Data mining of medical patient records
Software architectures
Electronic Health Records

Understanding Users

Identifying and addressing stakeholder needs
Usability and acceptability
Barriers and enablers to adoption
Social implications of pervasive health technology, and social inclusion
Coverage and delivery of pervasive healthcare services
Patient and caregiver empowerment
Diversity: population and condition-specific requirements
Inclusive research and design: engaging underrepresented populations
Digital interventions and health behavior change

Applications

Autonomous systems to support independent living
Clinical applications, validation and evaluation studies
Telemedicine and mHealth solutions
Chronic disease and health risk management applications
Health/Wellbeing promotion and disease prevention
Home based health and wellness measurement and monitoring
Continuous vs event-driven monitoring of patients
Smart homes and hospitals
Using mobile devices in the storage, update, and transmission of patient data
Wellbeing and lifestyle support
Systems to support individuals with auditory, cognitive, or vision impairments
Systems to support caregivers

Pervasive Healthcare Management

Challenges surrounding data quality
Standards and interoperability in pervasive healthcare
Business cases and cost issues
Security and privacy issues
Training of healthcare professional for pervasive healthcare
Legal and regulatory issues
Staffing and resource management

Scopus (CiteScore 2021: 2.0)
Ei Compendex
DOAJ
DBLP
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Special Issues

Editors-in-Chief
Nenad Filipovic, University of Kragujevac, Serbia
Jun Hu, Eindhoven University of Technology, The Netherlands
Area Editors
Alberto Antonietti (École Polytechnique Fédérale de Lausanne, Switzerland)
Alejandro Dominguez Rodriguez (Autonomous University of Baja Califo…

Editors-in-Chief
Nenad Filipovic, University of Kragujevac, Serbia
Jun Hu, Eindhoven University of Technology, The Netherlands
Area Editors
Alberto Antonietti (École Polytechnique Fédérale de Lausanne, Switzerland)
Alejandro Dominguez Rodriguez (Autonomous University of Baja California)
Alessandro Ruggiero (University of Salerno, Italy)
Alexandros Tzallas (Technological Educational Institute of Epirus)
Andjela Blagojevic (University of Kragujevac, Serbia)
Anind Dey (University of Washington)
Ansar-Ul-Haque Yasar (Hasselt University, Belgium)
Antonella Carbonaro (Universita' di Bologna, Italy)
Arijit Ukil (Tata Consultancy Services)
Asimina Kiourti (The Ohio State University)
AtaUllah Ghafoor (National University of Modern Languages, Islamabad, Pakistan.)
Bert Arnrich (Boğaziçi University, Istanbul, Turkey)
Boban Stojanovic (University of Kragujevac, Serbia)
Branko Arsic (University of Kragujevac, Serbia)
Constantinos Pattichis (University of Cyprus, Cyprus)
Daojing He (South China University of Technology, China)
Djordje Jakovljevic (Coventry University, UK)
Eduard Babulak (National Science Foundation, USA)
Emilija Stojmenova (Duh University of Ljubljana)
Emilio Serrano (Technical University of Madrid, Spain)
Frank Wallhoff (Jade University of Applied Sciences)
Giorgos Giannakakis (Foundation for Research and Technology Hellas, Greece)
Honggang Wang (UMASS. USA)
Kashif Saleem (King Saud University)
Laszlo Bokor (BME, Hungary)
Lazar Dasic (University of Kragujevac, Serbia)
Marcela Deyanira Rodriguez Urrea (Autonomous University of Baja California)
Marko Robnik-Sikonja (University of Ljubljana, Slovenia)
Mauro Femminella (University of Perugia, Italy)
Melina Frenken (Jade University of Applied Sciences)
Michalis Zervakis (Technical University of Crete, Greece)
Milos Ivanovic (University of Kragujevac, Serbia)
Milos Kotlar (University of Belgrade, Serbia)
Mohammad Upal Mahfuz (University of Wisconsin-Green Bay)
Mojtaba Taherisadr (University of Michigan)
Mosabber Uddin Ahmed (University of Dhaka)
Nadeem Javaid (COMSATS Institute of Information Technology, Islamabad, Pakistan)
Netzahualcoyotl Hernandez-Cruz (Oxford University, UK)
Nikolaos Bourbakis, Wright State University in Ohio, United States
Pan Zheng, University of Canterbury, New Zealand
Pietro Cipresso (Istituto Auxologico, Milan, Italy)
Razan Hamed (New York Institute of Technology, NY, USA)
Riccardo Martoglia (University of Modena and Reggio Emilia, Italy)
Saikishor Jangiti (SASTRA Deemed University, India)
Silvia Serino (Catholic University of Milan, Italy)
Stefan Rahr Wagner (Aarhus University)
Tanvir Zia (School of Computing & Mathematics, Charles Sturt University, Australia)
Tessa Dekkers (Delft University, The Netherlands)
Tijana Sustersic (Faculty of Engineering, University of Kragujevac)
Veljko Milutinovic (University of Belgrade, Serbia)
Venet Osmani (CREATE-NET, Trento, Italy)
Wilko Heuten (OFFIS, Germany)
Zhihan Lv (Qingdao University, China)
Zoran Bosnic (University of Ljubljana, Slovenia)
Ognjen Pavic (Institute for Information Technology, University of Kragujevac, Serbia)
Analúcia Schiaffino Morales (Federal University of Santa Catarina, Brazil)
Emeritus Editors-in-Chief
Gonçalo Marques, Polytechnic of Coimbra, Portugal

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/phat/index

Publisher: EAI
ISSN: 2411-7145
Volume: 7

Published: 2021-06-18

EUDL

m-health services for COVID-19 afflicted and infected victims

Fusion of Attentional and Traditional Convolutional Networks for Facial Expression Recognition

Review on the Role of Macular Edema in Retinopathy, Blindness and Automated Diagnosis Methods

Do information and communication technologies influence skills, competencies, health and well-being of teenagers? Current evidence

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

A Review of Medical Image Segmentation Algorithms

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