EAI Endorsed Transactions on Pervasive Health and Technology

Issue 26, 2021

Editor(s)-in-Chief: Manik Sharma

Articles
Information

Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19
Appears in:
phat 21(26): e1

Authors:
Chinmay Chakraborty, Arij Naser Abougreen

Abstract:
INTRODUCTION: Coronavirus disease (COVID-19) has recently emerged around the world. The beginning of the disease was in the Chinese city of Wuhan and then it has been spread and became a global epidemic. An early diagnosis of COVID-19 disease is absolutely necessary to control the epid…INTRODUCTION: Coronavirus disease (COVID-19) has recently emerged around the world. The beginning of the disease was in the Chinese city of Wuhan and then it has been spread and became a global epidemic. An early diagnosis of COVID-19 disease is absolutely necessary to control the epidemic.

OBJECTIVES: The aim of this paper is to present a review of the contribution of machine learning (ML) and IoT to confront the epidemic.

METHODS: Diagnosis using real-time reverse transcriptase-polymerase chain reaction (RT-PCR) is a definite diagnosis, but this method takes time, while a diagnosis using a computed tomography (CT) scan is a faster approach to diagnosis. However, a large number of patients need a CT scan, which puts a lot of pressure on the radiologist so visual fatigue may lead to diagnostic errors so there is an urgent need for additional solutions. Artificial intelligence (AI) is an efficient tool to combat COVID-19 disease. Computer scientists have been developing many systems to handle this epidemic.

RESULTS: It was found that ML is an efficient and powerful AI technology that can be used for trustworthy COVID-19 detecting and diagnosis from X-ray and CT images and it can be a potential method for diagnosis in the radiology department. In addition, ML can be used in segmentation, prediction purposes for COVID-19. Furthermore, ML can effectively support drug discovery procedure and can reduce clinical failures.

CONCLUSION: IoT has a significant role in monitoring an individual's health and COVID-19 diagnosis. This paper also highlights the challenges of employing ML and intelligent IoT for fighting COVID-19.
more »
Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm
Appears in:
phat 21(26): e2

Authors:
Anjali Kapoor, Rekha Agarwal

Abstract:
INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interv…INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interventions and surgical plans.

OBJECTIVES: The objective of this paper is to design and apply an enhanced brain tumor MRI segmentation using K-mean with K-means as machine learning based Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).

METHODS: A novel fitness function of Swarm Based PSO works on velocity variation is introduced, which enhances the segmented regions. The traditional k-means algorithm is enhanced by applying PSO to the segmented part. Another extension of Swarm Intelligence named Firefly is applied to compare the results of the PSO based segmentation, and Firefly based segmentation is used.

RESULTS: The simulation results are evaluated in terms of precision (98%), recall (0.95), f-measure (0.96), accuracy (97%), and segmentation time (2.63s) to measure the image segmentation the quality of main results obtained.

CONCLUSION: Comparative studies have shown that the proposed design using k-means combined with FA exhibited high accuracy and precision in detecting brain tumor RoI.
more »
Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model
Appears in:
phat 21(26): e3

Author:
Prasannavenkatesan Theerthagiri

Abstract:
INTRODUCTION: The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate. OBJECTIVES: This uses the artificial intelligence algorithm to design and …INTRODUCTION: The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate.

OBJECTIVES: This uses the artificial intelligence algorithm to design and develop the proposed confirmed, cured, deceased (COCUDE) model.

METHODS: A nonlinear auto-regressive model has been developed with several iterations to design the proposed COCUDE model. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics are analyzed to check the stationary and quality for the proposed COCUDE model.

RESULTS: The prediction results are evaluated by the performance error metrics such as mean square error (MSE) and root mean square error (RMSE), in which the errors are lower for the proposed model. Thus, the prediction results indicate the proposed COCUDE model might accurately predict future COVID-19 infection rates with reduced errors.

CONCLUSION: It might support the corresponding authorities to take precautious action on the required necessities for the medical and clinical infrastructures and equipment.
more »
Non-obtrusive 3d body tracking for automated mobility assessment in independently living older persons. Results of a pilot trial
Appears in:
phat 21(26): e4

Authors:
J. Lumetzberger, T. Münzer, M. Kampel

Abstract:
INTRODUCTION: With rising age, functional deficit and frequent falls may lead to long-term care admission. Mobility assessment tests can detect fall risk and may induce interventions that prevent a fall. OBJECTIVES: To assess mobility of older persons using real time data …INTRODUCTION: With rising age, functional deficit and frequent falls may lead to long-term care admission. Mobility assessment tests can detect fall risk and may induce interventions that prevent a fall.

OBJECTIVES: To assess mobility of older persons using real time data and to compare these data with the mobility assessment of physiotherapists.

METHODS: 20 older people aged 74±5 (mean ± SD) were monitored over 10 months to investigate the performance of an automated mobility tracker. Physiotherapists performed periodic mobility assessments. Annotated 3d recordings served as ground truth data.

RESULTS: High correlation (r=0.684) of annotated and tracked gait speed was found. The mean absolute error is 0.16 m/s.

CONCLUSION: 3D mobility trackers can be used to collect long-term mobility data. Since changes in mobility might indicate functional decline, long-term tracking allows to react to changes in mobility. Such a technology may have essential medical and social value.
more »
Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique
Appears in:
phat 21(26): e5

Authors:
Prasannavenkatesan Theerthagiri, Gopala Krishnan C, Nishan A H

Abstract:
INTRODUCTION: The sodium electrolyte deficiency in the human serum is known as Hyponatremia. The deficiency of sodium in the blood indulges many problems for the patients. If the sodium range in human serum not managed and treated it creates difficulties such as longer hospital stays and mortality.…INTRODUCTION: The sodium electrolyte deficiency in the human serum is known as Hyponatremia. The deficiency of sodium in the blood indulges many problems for the patients. If the sodium range in human serum not managed and treated it creates difficulties such as longer hospital stays and mortality.

OBJECTIVES: This paper focuses on forecasting the sodium ranges of patient using the machine learning algorithm supported by the past health records of the patients.

METHODS: The vital patient information including the disease history, age, gender, and serum sodium level before and after hospital admission are analysed using the logistic regression, k-nearest neighbour, multilayer perceptron, and extra-trees ensemble classification algorithm.The results of the classification algorithm show that the proposed MLP algorithm produces higher prediction results as compared to other machine learning algorithms. Also, the confusion matrix, Kappa score, R square value and error metrics.

CONCLUSION: The results show that the MLP classification is more suitable prognostic analysis of the hyponatremia for diseased patients.
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 2020: 0.7)
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DOAJ
DBLP
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Special Issues

Editors-in-Chief
Gonçalo Marques, Polytechnic of Coimbra, Portugal
Nenad Filipovic, University of Kragujevac, Serbia
Area Editors
Alberto Antonietti (École Polytechnique Fédérale de Lausanne, Switzerland)
Alejandro Dominguez Rodriguez (Autonomous University of Baja California)
Alessan…

Editors-in-Chief
Gonçalo Marques, Polytechnic of Coimbra, Portugal
Nenad Filipovic, University of Kragujevac, Serbia
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)

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: 2021-04-08

Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model

Non-obtrusive 3d body tracking for automated mobility assessment in independently living older persons. Results of a pilot trial

Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique

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