EAI Endorsed Transactions on Pervasive Health and Technology

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

Editor(s)-in-Chief: Gonçalo Marques and Nenad Filipovic

Aims & Scope
Indexing
Editorial Board
Special Issues

EAI Endorsed Transactions on Pervasive Health and Technology is open access, a peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining, knowledge management, IT applications to the 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. From 2021, the journal publishes five issues per year. Authors are not charged for article submission and processing.

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

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)

Recently Published
Most Popular

Hybrid Detection and Mitigation of DNS Protocol MITM attack based on Firefly algorithm with Elliptical Curve Cryptography
Appears in:
phat 23(1): e2

Authors:
Sabitha Banu. A., G. Padmavathi

Published:
19th Jan 2023

Abstract:
A Domain Name Server is a critical Internet component. It enables users to surf the web and send emails. DNS is a database used by millions of computers to determine which address best answers a user’s query. DNS is an unencrypted protocol that may be exploited in numerous ways. The most popular DN…A Domain Name Server is a critical Internet component. It enables users to surf the web and send emails. DNS is a database used by millions of computers to determine which address best answers a user’s query. DNS is an unencrypted protocol that may be exploited in numerous ways. The most popular DNS MITM attack uses DNS poisoning to intercept communications and fake them. DNS servers do not verify the IP addresses they forward traffic to. In DNS attacks, the attacker either targets the domain name servers or attempts to exploit system weaknesses. The Proposed FFOBLA-ECC model detects the DNS Spoofed nodes in a wireless network using the optimized firefly boosted LSTM with the help of TTL and RTR parameters received from the simulation environment and provides authentication between the nodes in order to mitigate it using the Elliptical curve cryptography. The proposed model results are different from the other methods and yield highly accurate results beyond 98% compared with the existing RF, ARF, and KNN methods.
more »
An Efficient Discrete Wavelet Transform Architecture with Low Power and Multiplier-Less Structure for Pervasive Biomedical Image Processing Application
Appears in:
phat 23(1): e1

Authors:
Maram Anantha Guptha, Surampudi Srinivasa Rao, Ravindrakumar Selvaraj

Published:
10th Jan 2023

Abstract:
INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagn…INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.
more »
Suicidal Behavior Prediction and Socioeconomic Suicide Indicators
Appears in:
phat 22(5): e5

Authors:
Muhammad Nouman, Kareem Ullah, Muhammad Azam

Published:
2nd Dec 2022

Abstract:
INTRODUCTION: According to the WHO (World Health Organization), nearly 0.8 million people commit suicide each year, with more than 20 suicide attempts for every self-immolation. Suicidal behaviors have a profound effect on communities, societies, families, friends, and colleagues. After the recogni…INTRODUCTION: According to the WHO (World Health Organization), nearly 0.8 million people commit suicide each year, with more than 20 suicide attempts for every self-immolation. Suicidal behaviors have a profound effect on communities, societies, families, friends, and colleagues. After the recognition of public health as a priority by the WHO, various studies are being conducted to prevent it.
OBJECTIVES: The investigation's goals were to improve understanding of suicide by identifying socioeconomic indicators correlated with rising suicide rates among divergent legions globally and to develop a prediction model for those who are at a higher risk of suicide by using different predictors of suicide such as tension, depression, anxiety, and so on.
METHODS: We used a variety of data mining techniques to create a prediction model for suicide, including Logistic Regression, Multilayer Perceptron, Polynomial/Gaussian/Sigmoid SVM, Decision Tree, and K-Nearest Neighbors. For identifying socioeconomic suicide indicators, we used various descriptive and exploratory analysis techniques such as mean, regression, and correlation.
RESULTS: Classification through the Gaussian Kernel - SVM has been shown to have the best results relative to others. Results also stated that many countries saw a decrease in suicide rates between 2006 and 2015, compared to 1996 to 2005. The highest concentrations have been reported in Europe, while the lower has been observed in South America.
CONCLUSION: Things are improving, at least according to the statistics. The performance of Gaussian Kernel-SVM has been demonstrated to be superior to the other algorithms for suicide prediction. Data on suicide and suicide attempts are imprecise and difficult to gather. Suicide and suicide attempt monitoring, and surveillance must be improved for suicide prevention initiatives to be effective.
more »
Evaluation of medical grade infusion pump parameters using Gaussian Process Regression
Appears in:
phat 22(5): e3

Authors:
J.V. Alamelu, A. Mythili

Published:
25th Nov 2022

Abstract:
Prediction techniques are extensively used in medical applications and health devices. The prediction of the infusion flow rate and its speed in a smart wireless infusion pump is necessary to provide precise drug flow. This paper has developed the prediction model to predict the lag time and infusi…Prediction techniques are extensively used in medical applications and health devices. The prediction of the infusion flow rate and its speed in a smart wireless infusion pump is necessary to provide precise drug flow. This paper has developed the prediction model to predict the lag time and infusion pump speed using the Gaussian process regression (GPR) technique with a squared exponential kernel. The present smart wireless infusion pump is usually incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as inputs to predict the pump speed, minimize start-up delays using proposed regression techniques. The evaluation of prediction models is done by the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared error (RMSE). These prediction results are verified for predicting lag time and infusion pump speed for two different carrier flowrates, 10 ml/hr, 50 ml/hr. The study's outcome indicates that the regression model GPR has better prediction accuracy with a mean coefficient of determination of 0.99. Hence, the GPR technique can achieve quick infusion speed with minimized lag time,
the optimal flow rate for smart infusion pumps.
more »
Automated Detection of fundus retinal image using EGODD algorithm
Appears in:
phat 22(5): e2

Authors:
Subha K., S. Kother Mohideen

Published:
12th Nov 2022

Abstract:
The developed device is tested for optical disc identification in the retinal image of the fundus. The theme chosen was interesting, as optical disc detection plays a critical role in retinal image processing for recognizing other fundus structures and is essential for identifying eye-related disea…The developed device is tested for optical disc identification in the retinal image of the fundus. The theme chosen was interesting, as optical disc detection plays a critical role in retinal image processing for recognizing other fundus structures and is essential for identifying eye-related diseases. Because human visual vision has been little researched for optical disc detection and an effort to extract data analytics based on eye eyes, the proposed study first discovers how human perception functions for optical disc detection using a bottom-up visual focus paradigm. Eye-view data as the user conducts basic target-search tasks are obtained from separate user groups made up of experts and no expert groups. Extensive data processing has been carried out to extract eye gazing characteristics such as fixation and using computer approach mark regions in the fundus' retinal picture. Segregated labeled data was used to build up a piece of top-down information to bend the search map to the target area. The resulting Eye-based Optical Disk Detection System (EGODD) was tested through normal machine learning algorithms. This proposed framework has been tested
and approved for optical disc detection; however, it can also be applied to other applications.
more »
Augmentation of Predictive Competence of Non-Small Cell Lung Cancer Datasets through Feature Pre-Processing Techniques
Appears in:
phat 22(5): e1

Authors:
M. Sumalatha, Latha Parthiban

Published:
2nd Nov 2022

Abstract:
The major Objective of the Study is to augment the predictive analytics of Non-Small Cell Lung Cancer (NSCLC) datasets with Feature Pre-Processing (FPP) technique in three stages viz. Remove base errors with common analytics on emptiness or non-numerical or missing values in the dataset, remove rep…The major Objective of the Study is to augment the predictive analytics of Non-Small Cell Lung Cancer (NSCLC) datasets with Feature Pre-Processing (FPP) technique in three stages viz. Remove base errors with common analytics on emptiness or non-numerical or missing values in the dataset, remove repeated features through regression analysis and eliminate irrelevant features through clustering methods. The FPP Model is validated using classifiers like simple and complex Tree, Linear and Gaussian SVM, Weighted KNN and Boosted Trees in terms of accuracy, sensitivity, specificity, kappa, positive and negative likelihood. The result showed that the NSCLC dataset formed after FPP outperformed the raw NSCLC dataset in all performance levels and showed good augmentation in predictive analytics of NSCLC datasets. The research proved that preprocessing is essential for better prediction of complex medical datasets.
more »
Analysis of the Role of Individuals and Governments in Post-COVID-19 Health Issues: The Case of Korea
Appears in:
phat 22(5): e4

Author:
Young-Chool Choi

Published:
25th Oct 2022

Abstract:
This study was conducted in order to ascertain what role government and individuals should play in the event of a pandemic such as Coronavirus occurring in Korea in the future, using information deriving from news articles available at the Bigkinds news portal site in Korea. The analysis period ran…This study was conducted in order to ascertain what role government and individuals should play in the event of a pandemic such as Coronavirus occurring in Korea in the future, using information deriving from news articles available at the Bigkinds news portal site in Korea. The analysis period ran from 11 March 2020, when the pandemic was declared by the World Health Organization, to 31 January 2023, almost three years later. Text mining analysis was conducted on all the articles, as a result of which six important roles that individuals should play, and ten roles that government should play, in a pandemic situation were suggested.
more »
Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals
Appears in:
phat 22(4): e5

Authors:
S. V. Mahesh Kumar, P. Dhinakar, R. Nishanth

Published:
22nd Sep 2022

Abstract:
INTRODUCTION: Aortic stenosis (AS) is a severe complicated heart valve disease. This valve abnormality is a slow-progressive condition and mostly asymptomatic. Hence, there is a need for a rapid non-invasive diagnosis method with minimal feature extraction. OBJECTIVE: In this paper, we proposed a …INTRODUCTION: Aortic stenosis (AS) is a severe complicated heart valve disease. This valve abnormality is a slow-progressive condition and mostly asymptomatic. Hence, there is a need for a rapid non-invasive diagnosis method with minimal feature extraction.
OBJECTIVE: In this paper, we proposed a spectral features-based rapid heart sound signal analysis method to identify the AS stages with minimum number of features.
METHODS: In this study, the heart sound signals were collected from the medical database and transformed into the frequency domain for further spectral feature analysis. We used the windowing technique to conditioning the heart signals before spectral analysis. The spectral statistical features were extracted from the computed frequency spectrum. The range of statistical features was compared for normal, early, and AS sound signals.
RESULTS: In experiments, the normal, early, and delayed AS heart sound signals were used. The normal/unhealthy condition of a heart was identified using the statistical features of the frequency spectrum. The experimental results show the statistical difference between the normal and AS heart sound signal spectrums.
CONCLUSION: The experimental results confirmed that the statistical features derived from the heart sound signal spectrums were varied according to the AS condition. Hence, the spectral statistical features can be considered as rapid predictors of AS.
more »
Prediction of Emergency Mobility Under Diverse IoT Availability
Appears in:
phat 22(4): e2

Authors:
Bin Sun, Renkang Geng, Yuan Xu, Tao Shen

Published:
13th Sep 2022

Abstract:
INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet of Things (IoT) develops at a high speed, which influences the quality and quantity of data, manageable resources and algorithms. OBJECTIVES: This work investigates differences in dynamic emergency mobility…INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet of Things (IoT) develops at a high speed, which influences the quality and quantity of data, manageable resources and algorithms.
OBJECTIVES: This work investigates differences in dynamic emergency mobility prediction when facing dynamic temporal IoT data with different quality and quantity considering diverse computing resources and algorithm availability.
METHODS: A node construction scheme under a small range of traffic networks is adopted in this work, which can effectively convert the road to graph network structure data which has been proved to be feasible and used for the small-scale traffic network data here. Besides, two different datasets are formed using public large scale traffic network data. Representative widely used and proven algorithms from typical types of methods are selected respectively with different datasets to conduct experiments.
RESULTS: The experimental results show that the graphed data and neural network algorithm can deal with the dynamic time series data with complex nodes and edges in a better way, while the non-neural network algorithm can predict the with a simple graph network structure.
CONCLUSION: Our proposed graph construction with graph neural network improves dynamic emergency mobility prediction. The prediction should consider the scenarios of availability of computing resources, quantity and quality of data among other IoT features to improve the results. Later, automation and data enrichment should be improved.
more »
Hybrid Detection and Mitigation of DNS Protocol MITM attack based on Firefly algorithm with Elliptical Curve Cryptography
Appears in:
phat 22(4): e3

Authors:
Sabitha Banu. A., Dr. G. Padmavathi

Published:
25th Aug 2022

Abstract:
A Domain Name Server is a critical Internet component. It enables users to surf the web and send emails. DNS is a database used by millions ofcomputers to determine which address best answers a user’s query. DNS is an unencrypted protocol that may be exploited in numerous ways. The mostpopular DNS …A Domain Name Server is a critical Internet component. It enables users to surf the web and send emails. DNS is a database used by millions ofcomputers to determine which address best answers a user’s query. DNS is an unencrypted protocol that may be exploited in numerous ways. The mostpopular DNS MITM attack uses DNS poisoning to intercept communications and fake them. DNS servers do not verify the IP addresses they forwardtraffic to. In DNS attacks, the attacker either targets the domain name servers or attempts to exploit system weaknesses. The Proposed FFOBLA-ECC model detects the DNS Spoofed nodes in a wireless network using the optimized firefly boosted LSTM with the help of TTL and RTR parametersreceived from the simulation environment and provides authentication between the nodes in order to mitigate it using the Elliptical curve cryptography. The proposed model results are different from the other methods and yield highly accurate results beyond 98% compared with the existing RF, ARF, and KNN methods.
more »

A Guide to Using Asynchronous Remote Communities (ARC) for Researching Distributed Populations
Appears in:
phat 17(11): e4

Authors:
Haley MacLeod, Ben Jelen, Annu Prabhakar, Lora Oehlberg, Katie Siek, Kay Connelly

Downloads:
6177

Abstract:
Existing research methods are largely intended to be conducted co-located and synchronously with a study population, but this approach is not feasible with remote or distributed populations. We describe a needs assessment study we conducted on Facebook. We report on our use of adapted versions of c…Existing research methods are largely intended to be conducted co-located and synchronously with a study population, but this approach is not feasible with remote or distributed populations. We describe a needs assessment study we conducted on Facebook. We report on our use of adapted versions of commonly used HCI research methods and lessons learned from this approach.
more »
An Efficient Face Mask Detector with PyTorch and Deep Learning
Appears in:
phat 21(25): e4

Authors:
CMAK. Zeelan Basha, B.N. Lakshmi Pravallika, E. Bharani Shankar

Downloads:
3113

Abstract:
INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an econom…INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an economic catastrophe, recession, and much more. One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world. But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes. As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.

OBJECTIVES: To develop aFace Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.

METHODS: A Neural Network model called ResNet is trained on the dataset. Furthermore, this work makes use of the inbuilt Face Detector after training. Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.

RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.

CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.
more »
Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study
Appears in:
phat 19(19): e2

Authors:
Iqra Nissar, Danish Raza Rizvi, Sarfaraz Masood, Aqib Nazir Mir

Downloads:
2908

Abstract:
INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning (ML) has helped in solving problems of computer vision, natural la…INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of
the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning
(ML) has helped in solving problems of computer vision, natural language processing, speech recognition etc.

OBJECTIVES: This paper aims to analyse the effect of feature type selection i.e. MFCC and TQWT on the efficiency of
voice based PD detection system along with the use an ensemble learning based classifier for this task.

METHODS: Hence, in this work, various machine learning models, including Logistic Regression, Naive Bayes, KNN,
Random Forest, Decision Tree, SVM, MLP, and XGBoost, have been employed and explored for PD detection purpose.
The task of Feature selection was also done using minimum-Redundancy and Maximum-Relevance (mRMR) and
Recursive Feature Elimination (RFE) techniques.

RESULTS: The results of the XGBoost with mRMR feature selection, outperformed all other models with a high accuracy
of 95.39% and precision, recall and F1-score of 0.95 each, when both MFCC and TQWT features were selected.

CONCLUSION: The results obtained strongly support the use of XGBoost model for the voice sample based detection of
PD along with mRMR feature selection technique.
more »
Security and Privacy Issues with IoT in Healthcare
Appears in:
phat 18(14): e2

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
2804

Abstract:
In healthcare, the Internet of Things (IoT) oﬀers many beneﬁts, including being able to monitor patients more closely and using data for analytics. When it comes to IoT for medical device integration, the focus is shifted towards the consumer end, such as glucose meters, blood pressure cuﬀs, and ot…In healthcare, the Internet of Things (IoT) oﬀers many beneﬁts, including being able to monitor patients more closely and using data for analytics. When it comes to IoT for medical device integration, the focus is shifted towards the consumer end, such as glucose meters, blood pressure cuﬀs, and other devices designed to record data on patient vital signs. This enables healthcare providers to automatically collect information and apply decision support rules to allow for earlier intervention in the treatment process. Unfortunately, medical companies often do not consider the security risks of connecting these devices to the internet. There is a possibility that a zero-day exploit in a medical device can be used to injure or even kill someone without being detected. The rise in hackable medical devices has forced the FDA to issue formal guidance on how medical device makers should handle reports about cyber vulnerabilities. This paper aims to explore the role of IoT in healthcare, vulnerabilities, attacks, and security issues and solutions.
more »
Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis
Appears in:
phat 18(15): e1

Authors:
Manik Sharma, Natalia Romero

Downloads:
2365

Abstract:
more »
Ear-Lead Multiple Smart Bio Sensor System in M-health
Appears in:
phat 16(6): e5

Authors:
Numan Celik, Wamadeva Balachandran, Nadarajah Manivannan

Downloads:
2212

Abstract:
In this paper, we present a multi-parameter wearable sensor system in conjunction with a smartphone to enable a real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical research illustrating that…In this paper, we present a multi-parameter wearable sensor system in conjunction with a smartphone to enable a real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical research illustrating that continuing accurate measurements of core body temperature (CBT) are crucial to investigate human thermoregulation in ambulatory environment and during activity. On the other hand, ECG remains the mainstay test for primary diagnosis and survival analysis of heart diseases. We implement a wireless multisensory system that measures both the tympanic temperature inside the ear and ECG data from an ear and an arm. The behind-the-Ear device makes desirable to measure ECG data without any chest belt and allow the patient to move around freely. The proposed Bluetooth device has similarity of a hearing aid and is wirelessly connected to a smartphone for data transmission and displaying. This device not only gives access to the core temperature and ECG data in real time, but also the device can be controlled – removed and reapplied – by the patient at any time, thus increasing the performance of personal health applications.
more »
Overcoming Barriers to Healthcare Access and Delivery
Appears in:
phat 18(15): e2

Authors:
Foluso Ayeni, Zacchaeus Omogbadegun, Nicholas Omoregbe, Sanjay Misra, Lalit Garg

Downloads:
2181

Abstract:
Access to standard and economical health care delivery, availability of significant health information are viewed as some of the most functional public health interventions in present-day history. Despite that, current information obtained from the WHO regarding Nigeria’s health condition shows tha…Access to standard and economical health care delivery, availability of significant health information are viewed as some of the most functional public health interventions in present-day history. Despite that, current information obtained from the WHO regarding Nigeria’s health condition shows that the average existence expectancy is at 54 years and maternal mortality at 608 per 100,000 live births as a result of poor health care services. Several aspects of health informatics have been applied to solve these challenges such as the transformation of records from manual to electronic. Among these are the telemedicine and socialized healthcare, which have been barely adopted in developing nations. This work thus proposes an architectural framework for a cloud-supported socialized healthcare system. In order to achieve this; a webbased application software was designed and implemented through the use of cloud computing technology platforms and server side scripting tools. This study proves that socialized healthcare will really go a long way in defeating barriers of viable human access and delivery.
more »
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training
Appears in:
phat 20(24): e2

Authors:
Saravanan. S, Sujitha Juliet

Downloads:
1992

Abstract:
INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology. Autoencoder, an unsupervised learning algorithm with the input value as same as that of the output value, is considered in this research wo…INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology. Autoencoder, an unsupervised learning algorithm with the input value as same as that of the output value, is considered in this research work for effective medical image reconstruction.

OBJECTIVES: Medical data needs to be reconstructed without distorting the details present over it. A deep neural network that accepts the data and processes it to the other several layers and reconstructs that data is achieved by autoencoder.

METHODS: Deep Autoencoder is implemented in this methodology as it has been considered for high dimensionality reduction. Layer by layer pretraining is achieved using an approximate inference algorithm called Deep Boltzmann Machine.

RESULTS: The proposed method proves to be efficient when compared with the performance of the other autoencoders such as Deep Autoencoder with multiple Backpropagation (DA-MBP), Deep Autoencoder with RBM (DA-RBM) and Deep Convolutional Autoencoder with RBM (DCA-RBM).

CONCLUSION: Performance metrics are measured in terms of Mean Square Error, Structural similarity Index and PSNR.
more »
Low-threshold CMOS Rectifier Design for Energy Harvesting in Biomedical Sensors
Appears in:
phat 16(6): e4

Authors:
Mehmet Yuce, Ali Mohammadi, Jean-Michel Redoute

Downloads:
1975

Abstract:
The power transfer efficiency of energy harvesting systems is strongly dependent on the power conditioning circuits, especially rectifiers. The voltage drop across rectifier and its leakage current can drastically influence the efficiency. The hybrid energy harvesters impose even more severe co…The power transfer efficiency of energy harvesting systems
is strongly dependent on the power conditioning circuits,
especially rectifiers. The voltage drop across rectifier and
its leakage current can drastically influence the efficiency.
The hybrid energy harvesters impose even more severe constraints on the rectifier. The low Vth transistors and bulk
regulation technique are used in this work to mitigated the
voltage drop and leakage current, respectively. It has been
shown that the bulk regulation stops the current leakage
through body of PMOS transistor. A near zero threshold
cross connected CMOS rectifier is presented in this work using the standard 180nm UMC technology and experimental
analysis are carried out to evaluate the circuit performance.
more »
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

Downloads:
1896

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 »

Publisher: EAI
ISSN: 2411-7145
Number of Volumes: 8

Last Published: 2022-12-20