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 Manik Sharma

Aims & Scope
Indexing
Editors in Chief
Editorial Board

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.

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

Anind Dey (University of Washington)
Ansar-Ul-Haque Yasar (Hasselt University, Belgium)
Alejandro Dominguez Rodriguez (Autonomous University of Baja California)
Alexandros Tzallas (Technological Educational Institute of Epirus)
Asimina Kiourti (The Ohio State University)
AtaUllah Ghafoor (National University of Modern Languages, Islamabad, Pakistan.)
Arijit Ukil (Tata Consultancy Services)
Bert Arnrich (Boğaziçi University, Istanbul, Turkey)
Daojing He (South China University of Technology, China)
Emilija Stojmenova (Duh University of Ljubljana)
Frank Wallhoff (Jade University of Applied Sciences)
Honggang Wang (UMASS. USA)
Kashif Saleem (King Saud University)
Laszlo Bokor (BME, Hungary)
Marcela Deyanira Rodriguez Urrea (Autonomous University of Baja California)
Mauro Femminella (University of Perugia, Italy)
Melina Frenken (Jade University of Applied Sciences)
Mohammad Upal Mahfuz (University of Wisconsin-Green Bay)
Mosabber Uddin Ahmed (University of Dhaka)
Mojtaba Taherisadr (University of Michigan)
Nadeem Javaid (COMSATS Institute of Information Technology, Islamabad, Pakistan)
Pietro Cipresso (Istituto Auxologico, Milan, Italy)
Razan Hamed (New York Institute of Technology, NY, USA)
Silvia Serino (Catholic University of Milan, Italy)
Stefan Rahr Wagner (Aarhus University)
Tanvir Zia (School of Computing & Mathematics, Charles Sturt University, Australia)
Venet Osmani (CREATE-NET, Trento, Italy)
Wilko Heuten (OFFIS, Germany)
Tessa Dekkers (Delft University, The Netherlands)
Saikishor Jangiti (SASTRA Deemed University, India)
Zhihan Lv (Qingdao University, China)
Antonella Carbonaro (Universita' di Bologna, Italy)
Giorgos Giannakakis (Foundation for Research and Technology Hellas, Greece)
Alessandro Ruggiero (University of Salerno, Italy)
Eduard Babulak (National Science Foundation, USA)
Emilio Serrano (Technical University of Madrid, Spain)
Riccardo Martoglia (University of Modena and Reggio Emilia, Italy)
Alberto Antonietti (École Polytechnique Fédérale de Lausanne, Switzerland)
Netzahualcoyotl Hernandez-Cruz (Ulster University, UK)

Gonçalo Marques¹

Gonçalo Marques holds a PhD in Computer Science Engineering and is a member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia, and database systems. Furthermore, he worked as a Software Engineer in the Innovation and Development unit of Groupe PSA automotive industry from 2016 to 2017 and in the IBM group from 2018 to 2019. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 60 publications in international journals and conferences, is a frequent reviewer of journals and international conferences, and is also involved in several edited books projects.

Manik Sharma²

Dr. Manik Sharma is an MCA, UGC-NET qualified and has received his Ph.D. degree in “Distributed Query Optimization”. He has more than 12 years of teaching experience. He is working in the area of Data Mining, Machine Learning and Soft Computing with specialization in the diagnosis of diseases and modeling of DSS query optimizer. The thrust area is to improve the predictive accuracy in early diagnosis of several fatal human disorders. He has published 3 books related to Computer Network, Internet Applications, Management Information System and Database Theory. In addition, he has also published more than 25 research papers in various journals of International repute including Scopus and Thomson Reuters. He has also presented more than ten research articles in various national and international UGC and IEEE conferences. Moreover, he is an active member of International Program Committees, Technical Program Committees, and Advisory Committees of several academics conferences (United Kingdom, Singapore, Mauritius, Thailand, Vietnam, USA, Hong Kong, Canada, UAE, and India).

1: Polytechnic of Coimbra, Portugal
2: DAV University, India

Recently Published
Most Popular

A Model-Driven Optical Clinic Management Systems: Systematic Software Engineering Approach
Appears in:
phat 22(30): e5

Authors:
Adams Addison Kobla Azameti, Godfred Koi-Akrofi, Nelson Agbodo, Julius K. Amegadzie

Published:
16th Mar 2022

Abstract:
INTRODUCTION: eHealth systems in a modern hospital and clinic require stringent measures to coordinate the operations of doctors, nurses, pharmacies for improved health care delivery. OBJECTIVES: The primary objective is to perform a comparative analysis to devise a novel approach to address t…INTRODUCTION: eHealth systems in a modern hospital and clinic require stringent measures to coordinate the operations of doctors, nurses, pharmacies for improved health care delivery.

OBJECTIVES: The primary objective is to perform a comparative analysis to devise a novel approach to address the needs of a hospital information management system. This has triggered an urgent response to develop Optical Clinic Management
System (OCMS) to address the limitation of the existing system. This intervention would promote the good health and well-being of humankind to meet the Sustainable Development Goals 3 (SDGs 3).

METHODS: The study proposed a Design Science Research Methodology (DSRM) approach in Software Engineering as a catalyst to design OCMS to capture patients’ up-to-date records for medical diagnosis. The system is to assist Clinicians to prescribe medications based on a patient’s medical history by clicking a computer button.

RESULTS: The limitations discovered during systems analysis and design of the existing systems were addressed during system evaluation and testing. It was observed that the proposed optical clinic management systems received a 98% acceptance
for the implementation.

CONCLUSION: This study explores the problem facing clinic and hospital administration and established major factors affecting the existing systems. It was discovered that the paper-based management systems used to keep patients’ medical
records were found to be unreliable and therefore unsafe to be used as the basis to prescribe medication for patients, hence the need for this comprehensive system to address the problem for effective health care delivery. The situation in the existing system incidentally led to misplaced and unstructured handling of patient clinical records that may inadvertently make the clinicians administer medications with no reference to the patient’s previous diagnosis due to the lost file. Hence, the aftermath of the Covid-19 pandemic and its global destruction of human lives should motivate African leaders to invest adequate resources in the development of information technology applications for robust health information systems to improve health care delivery in Africa.
more »
Increasing motivation for cancer treatment adherence in children through a mobile educational game: a pilot study
Appears in:
phat 22(30): e4

Authors:
Carmen Chai Wang Er, Lau Bee Theng, Mark Tee Kit Tsun, Abdullah Al Mahmud

Published:
15th Feb 2022

Abstract:
INTRODUCTION: It is crucial to educate childhood cancer patients (CCPs) about their illness and motivate them for cancer treatment and treatment side-effects management. OBJECTIVES: This paper describes the design, development and pilot evaluation of the proposed serious game intervention wi…INTRODUCTION: It is crucial to educate childhood cancer patients (CCPs) about their illness and motivate them for cancer
treatment and treatment side-effects management.

OBJECTIVES: This paper describes the design, development and pilot evaluation of the proposed serious game intervention
with CCPs in Malaysia.

METHODS: A single-centre, single-arm intervention was conducted with CCPs (n=8). Surveys were done pre-test and post-test.

RESULTS: The Protection Motivation Theory was used to measure the participants' motivation. Self-reported surveys with
CCPs and caregiver dyads showed a significant increase in participants’ intention to use cancer treatment. Although the
increase in the intention to use daily self-care and cancer knowledge survey scores was not substantial, the post-test
caregivers' feedback revealed that the game was beneficial for their children.

CONCLUSION: Early results of the study have shown the intervention’s potential to boost the knowledge and motivations
of CCPs.
more »
A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach
Appears in:
phat 22(30): e3

Authors:
L Kanya Kumari, B Naga Jagadesh

Published:
11th Jan 2022

Abstract:
INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient’s health can be improved. OBJ…INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early
stages so that the patient’s health can be improved.

OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Co-occurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms.

METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is
used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality.

RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural
Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset.

CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods. In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification.
more »
Influence of Motivational Design Techniques on Use and Acceptance of Self-Management Health Systems in Older Adults
Appears in:
phat 22(30): e2

Authors:
Ine D’Haeseleer, Jamie Schoofs, Kim Schutters, Dominique Schreurs, Vero Vanden Abeele

Published:
24th Nov 2021

Abstract:
INTRODUCTION: Self-Management Health Systems (SMHS) are envisioned to support older adults and contribute to ageing in place. To promote the use and acceptance of SMHS, designers may resort to using motivational design techniques (MDT). OBJECTIVES: This paper investigates how and which older a…INTRODUCTION: Self-Management Health Systems (SMHS) are envisioned to support older adults and
contribute to ageing in place. To promote the use and acceptance of SMHS, designers may resort to using
motivational design techniques (MDT).

OBJECTIVES: This paper investigates how and which older adults prefer MDT in SMHS and whether these
preferences are mediated by either motivation for health management (MHM) or mobile device proficiency (MDP).

METHODS: Based on a user evaluation with 45 older adults (mean age=84, SD=6.75), 32 MDT, MHM, and
MDP were polled through questionnaires and informal interviews.

RESULTS: Findings showed that most techniques were welcomed, but also that scores vary widely, reflecting heterogeneity in the population and calling for caution in its implementation. These findings are then translated into implications for design.

CONCLUSION: Overall, older adults welcome MDT in SMHS. However, MDT are no panacea; tailoring MDT
to users’ needs is therefore crucial.
more »
A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones
Appears in:
phat 22(30): e1

Authors:
Umair Saeed, Kamlesh Kumar, Asif Ali Laghari, Mansoor Ahmed Khuhro, Noman Islam, Ghulam Muhammad Shaikh, Fahad Hussain, Aftab Ahmed Shaikh

Published:
2nd Nov 2021

Abstract:
INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate. These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS. By using these sensors to analyze the activity of the end-use…INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate. These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS. By using these sensors to analyze the activity of the end-user, behavioural characteristics of the user can be captured.

OBJECTIVES: Although smart-phone and wearable devices provide a platform for conducting social, psychological and physical studies, they still have several limitations and challenges.

METHODS: This paper provides a comparative analysis of different classical Machine Learning and Deep Learning algorithms and discusses their accuracy and efficiency for human activity recognition (HAR).

RESULTS and CONCLUSION: The paper has primarily used the data captured using wireless sensor devices placed on different parts of a human body, and then compared the results for different classifiers. The conclusion shows that Deep learning schemes are extremely accurate and efficient in comparison with classical machine learning techniques.
more »
An interactive VR platform with emotion recognition for self-attachment intervention
Appears in:
phat 21(29): e5

Authors:
Neophytos Polydorou, Abbas Edalat

Published:
14th Sep 2021

Abstract:
INTRODUCTION: Self-attachment is a new self-administrable psychotherapeutic intervention based on creating an affectional bond between the user and their childhood-self using their childhood photos to develop the capacity for affect self-regulation. Technological advances, such as virtual reality …INTRODUCTION: Self-attachment is a new self-administrable psychotherapeutic intervention based on creating an affectional bond between the user and their childhood-self using their childhood photos to
develop the capacity for affect self-regulation. Technological advances, such as virtual reality (VR), can enhance the procedure of this intervention and make it scalable.

METHODS: We have developed a user-friendly, interactive VR platform for self-attachment featuring a
virtual assistant and a customised child avatar that resembles the user in their childhood. The virtual agent interacts with the user and using an emotion recognition algorithm can provide suggestions for the user to undertake an appropriate self-attachment sub-protocol. Furthermore, the platform allows user interaction with the child avatar, such as embracing the avatar.

RESULTS: We show by a small preliminary trial that such a VR experience can be realistic, leading to
a positive emotion change in the user.
more »
Deep Learning Based Analysis of Ophthalmology: A Systematic Review
Appears in:
phat 21(29): e4

Authors:
Puneet Kumar, R. Kumar, M. Gupta

Published:
14th Sep 2021

Abstract:
INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment’s it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and be…INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment’s it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less.

OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc.

METHODS: AI and Machine Learning Technique are used to conduct this research for analysis.

RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology.

CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.
more »
Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
Appears in:
phat 21(29): e3

Authors:
Nagaraj M. Lutimath, Neha Sharma, Byregowda B K

Published:
30th Aug 2021

Abstract:
INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem. OBJECTIVES: The heart disease is cardiovascular disease, with a set…INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem.

OBJECTIVES: The heart disease is cardiovascular disease, with a set of conditions affecting the heart. During heart disease,
there will be heartbeat problems with congenital heart disorders and coronary artery defects. A coronary heart defect is a
heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is
necessary to analyse the prediction of heart attack based on the symptoms.

METHODS: The available data set of patients with heart defects symptoms is taken and analysed in this paper using the
random forest and decision tree regression models. The missing data is updated using mean value of the attribute. Python
language is used to predict the accuracy.

RESULTS: Three performance measures are taken for analysing the available UCI Cleveland data set for heart disease. The
performance measures are the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Vital attributes of
the data set are taken for analyses using the random forest regression model and decision tree regression model. The analyses
shows that the slope attribute provides the better prediction for the heart disease. The results are shows that the females are
more prone to heart attack.

CONCLUSION: Prediction of heart disease using the UCI machine learning data set at Cleveland repository is analysed
using random forest regression model and decision tree regression models. We find random forest regression model provides
better accuracy than decision tree regression model.
more »
Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients
Appears in:
phat 21(29): e2

Authors:
Shiva Shankar Reddy, Gadiraju Mahesh, N. Meghana Preethi

Published:
24th Aug 2021

Abstract:
INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients. It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health…INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients.
It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work.

OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease.

METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming.

RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively.

CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers.
more »
A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms
Appears in:
phat 21(29): e1

Authors:
Mirza Muntasir Nishat, Fahim Faisal, Rezuanur Rahman Dip, Sarker Md. Nasrullah, Ragib Ahsan, Fahim Shikder, Md. Asfi-Ar-Raihan Asif, Md. Ashraful Hoque

Published:
13th Aug 2021

Abstract:
INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, the impairment is irreversible and imperceptible up until the disease reaches one of the later stages, demanding early detection and initiation of tre…INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, the impairment is irreversible and imperceptible up until the disease reaches one of the later stages, demanding early detection and initiation of treatment in order to ensure a good prognosis and prolonged life. In this aspect, machine learning algorithms have proven to be promising, and points towards the future of disease diagnosis.

OBJECTIVES: We aim to apply different machine learning algorithms for the purpose of assessing and comparing their accuracies and other performance parameters for the detection of chronic kidney disease.

METHODS: The ‘chronic kidney disease dataset’ from the machine learning repository of University of California, Irvine, has been harnessed, and eight supervised machine learning models have been developed by utilizing the python programming language for the detection of the disease.

RESULTS: A comparative analysis is portrayed among eight machine learning models by evaluating different performance parameters like accuracy, precision, sensitivity, F1 score and ROC-AUC. Among the models, Random Forest displayed the highest accuracy of 99.75%.

CONCLUSION: We observed that machine learning algorithms can contribute significantly to the domain of predictive analysis of chronic kidney disease, and can assist in developing a robust computer-aided diagnosis system to aid the healthcare professionals in treating the patients properly and efficiently.
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:
5971

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:
2769

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:
2466

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 »
Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis
Appears in:
phat 18(15): e1

Authors:
Manik Sharma, Natalia Romero

Downloads:
2178

Abstract:
more »
Security and Privacy Issues with IoT in Healthcare
Appears in:
phat 18(14): e2

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
2148

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

Authors:
Numan Celik, Wamadeva Balachandran, Nadarajah Manivannan

Downloads:
2104

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:
1967

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 »
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:
1881

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 »
Body-IMU autocalibration for inertial hip and knee joint tracking
Appears in:
phat 16(5): e3

Authors:
Sarvenaz Salehi, Gabriele Bleser, Attila Reiss, Didier Stricker

Downloads:
1614

Abstract:
Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems. In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to the adjacent body joints. To deriv…Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems.
In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to
the adjacent body joints. To derive an efficient, robust and precise method based on a practical procedure is a crucial as well as
challenging task when developing a wearable system with multiple
embedded IMUs. In this work, first, we perform a theoretical analysis of an existing position calibration method, showing its limited
applicability for the hip and knee joint. Based on this, we propose a method for simultaneously estimating the positions of three IMUs
(mounted on pelvis, upper leg, lower leg) relative to these joints.
The latter are here considered as an ensemble. Finally, we perform
an experimental evaluation based on simulated and real data, showing the improvements of our calibration method as well as lines of future work.
more »
Designing a resilient supply chain: An approach to reduce drug shortages in epidemic outbreaks
Appears in:
phat 20(21): e2

Authors:
Jose Antonio Lozano-Diez, Jose Antonio Marmolejo-Saucedo, Roman Rodriguez-Aguilar

Downloads:
1594

Abstract:
INTRODUCTION: Supply network design is a long-studied topic that has evolved to address disruptive situations. The risk of supply chain disruption leads to the development of resilient supply chains that are capable of reacting eﬀectively. OBJECTIVES: In the context of public health, drug suppl…INTRODUCTION: Supply network design is a long-studied topic that has evolved to address disruptive situations. The risk of supply chain disruption leads to the development of resilient supply chains that are capable of reacting eﬀectively.

OBJECTIVES: In the context of public health, drug supply networks face shortage challenges in many situations, such as current epidemic outbreaks such as COVID-19. Drug shortages can occur due to manufacturing problems, lack of infrastructure, and immediate reaction mechanisms.

METHODS: The case study is solved with anyLogistix optimization and simulation software.

RESULTS: We present the results of a hypothetical study on the impact of COVID-19 on a regional supply network. The results of this research are intended to be the basis for the design of resilient supply chains in epidemic outbreaks.

CONCLUSION: Drug providers should consider strategies to prevent or reduce the impact of shortages as well as disruption spreads.
more »

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

Last Published: 2022-03-16

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

Gonçalo Marques1

Manik Sharma2

Gonçalo Marques¹

Manik Sharma²