EAI Endorsed Transactions on Pervasive Health and Technology

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.

Editor(s)-in-Chief: Manik Sharma

Aims & Scope
Indexing
Editor 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)
Gonçalo Marques (Universidade da Beira Interior, Portugal)
Netzahualcoyotl Hernandez-Cruz (Ulster University, UK)

Manik Sharma¹

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: DAV University, India

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An interactive VR platform with emotion recognition for self-attachment intervention
Appears in:
phat 18: e10

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 18: e9

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 18: e8

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 18: e7

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 18: e6

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 »
ASSISTO eCARE 4.0 -- An IoT- and AI-based architecture for assisted active aging
Appears in:
phat 18: e5

Authors:
Leonardo Bertini, Dario Bruneo, Massimo Mecella, Emilia Reda

Published:
6th Aug 2021

Abstract:
INTRODUCTION: All over Europe, there is an increasing demand for social/welfare services and a shift towards a demand increasingly formed by a mix of well-being and safety. Artificial intelligence (AI), Internet-of-Things (IoT) and cloud computing techniologies can play a major role in such a typ…INTRODUCTION: All over Europe, there is an increasing demand for social/welfare services and a shift towards a
demand increasingly formed by a mix of well-being and safety. Artificial intelligence (AI), Internet-of-Things (IoT) and
cloud computing techniologies can play a major role in such a type of services.

OBJECTIVES: The aim of this work was to investigate, design, develop and validate a prototype platform, named Assisto
eCare 4.0, providing “well-being” and “safety” services/functionalities to home elderly residents.

METHODS: The platform builds upon biometric technologies and analytics functionalities exploiting AI techniques
in order to limit human intervention during emergencies and automatically and immediately deciding actions to be
performed by making the operators intervene also directly at the user home.

RESULTS: The prototype has been validated with a group of 22 users over a period of more than 7 months. The results
derived from the final evaluation questionnaire show that the majority of participants rated the service as excellent.

CONCLUSIONS: The platform has been released according to the API-as-a-Service model, proposing itself with a
pioneering model of social open innovation, which is to develop and test the IT system and then to make it available
to all those who want to use it. Currently (July 2021) the system has been engineered and offered by a consortium of
different industries and is operative in the Rome area.
more »
Cloud based IoT Smart Healthcare System for Remote Patient Monitoring
Appears in:
phat 18: e4

Authors:
G. Jaya Lakshmi, Mangesh Ghonge, Ahmed J. Obaid

Published:
15th Jul 2021

Abstract:
INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patien…INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care.

OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud.

METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely.

RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output.

CONCLUSION: Patient physical parameters like heart beat respiration ,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work.
more »
Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection
Appears in:
phat 18: e3

Authors:
G. N. Balaji, S. V. Suryanarayana, J. Sengathir

Published:
7th Jul 2021

Abstract:
Introduction: Cervical cancer is considered as the major curse to women society of the world due to their low survival rate. However, the prognosis of cervical cancer at an early stage through periodic screening was identified to enhance the survival probability of the women patients around the wor…Introduction: Cervical cancer is considered as the major curse to women society of the world due to their low survival rate. However, the prognosis of cervical cancer at an early stage through periodic screening was identified to enhance the survival probability of the women patients around the world.

Objective: In this paper, a Boykov-Kolmogorov Graph Cuts and Cloud Model-based Synergy Integrated Segmentation (BKGC-CMSIS) Technique for facilitating predominant cervical cancer detection from the pap smear images used for prognosis.

Methods: This proposed BKGC-CMSIS scheme introduces an effective Boykov-Kolmogorov Graph Cuts-based image partitioning method that estimates the image data through a synergy cloud model for formulating objective functions. The objective function used in this proposed BKGC-CMSIS scheme includes a data item and a smooth term for boundary preservation in order to determine the deviation of each pixel corresponding to the different regions of the cervical pap smear cells.

Results: Also, it identifies the data item through the utilization of X-condition cloud generator for determining and defining the accurate boundaries of cytoplasm and nuclei derived from the pap smear cells. This proposed BKGC-CMSIS scheme uses the merits of membership degree through the incorporation of the smooth term for estimating the degree of similarity existing among the neighboring regions of the cervical pap smear cells.

Conclusion: The experimental results of this proposed BKGC-CMSIS scheme is also potent in enhancing the classification accuracy by 14% superior to the benchmarked cervical cancer detection schemes considered for investigation.
more »
An Empirical Study on Challenges Faced by the Elderly in Care Centres
Appears in:
phat 18: e2

Authors:
Jie Li, Wei Wei Goh, NZ Jhanjhi, Filzah Binti Md Isa, Sumathi Balakrishnan

Published:
11th Jun 2021

Abstract:
INTRODUCTION: The fact of our lives is that we will be reaching to elderly life stage sooner or later. Our elderly community has been left far behind of new technology updates. Technology advances just as age is catching up on us. To many who are in their twilight years, …INTRODUCTION: The fact of our lives is that we will be reaching to elderly life stage sooner or later. Our elderly community has been left far behind of new technology updates. Technology advances just as age is catching up on us. To many who are in their twilight years, using cutting edge technologies may be a hurdle in their activities of daily living (ADLs).

OBJECTIVES: Therefore, this study aims to identify and measure the ADL challenges that the elderly encounter and improve their quality of life (QoL).

METHODS: This research embarked on semi-structured interviews at 9 geriatric care centres in Malaysia to investigate the ADL challenges by the elderly residents. The thematic analysis approach was employed for data analysis and further discussion.

RESULTS: The research findings suggested that the QoL of the elderly is limited by the challenges of geriatric issues, poor living conditions, and technology acceptance barriers.

CONCLUSION: In conclusion, the current research provides an overview of the ADL challenges faced by the elderly with recommendations of user-centred Internet of Things (IoT) devices for elderly to use in an ambient assisted living (AAL) environment in Society 5.0.
more »
Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques
Appears in:
phat 18: e1

Authors:
Bhawna Dhruv, Neetu Mittal, Megha Modi

Published:
31st May 2021

Abstract:
INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and …INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast.

OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial.

METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor.

RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization.

CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.
more »

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

Author:
Manik Sharma

Published:
11th Jun 2021

Abstract:
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

Published:
12th Apr 2021

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 »
Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning
Appears in:
phat 21(27): e5

Authors:
Sanjeev Dhawan, Kulvinder Singh, Mamta Arora

Published:
12th Apr 2021

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

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

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

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

CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.
more »
Do information and communication technologies influence skills, competencies, health and well-being of teenagers? Current evidence
Appears in:
phat 21(27): e4

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

Published:
6th Apr 2021

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

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

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

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

CONCLUSION: Adopting ICT may have beneficial implications for teenagers, but it can pose more adverse effects on their development when used excessively.
more »
Review on the Role of Macular Edema in Retinopathy, Blindness and Automated Diagnosis Methods
Appears in:
phat 21(27): e3

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

Published:
17th Mar 2021

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

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

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

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

CONCLUSION: We justified the notion that it will be beneficial to have a method that can analyse the macula region and alert if there is any abnormality near that region to prevent vision loss.
more »
Fusion of Attentional and Traditional Convolutional Networks for Facial Expression Recognition
Appears in:
phat 21(27): e2

Authors:
Tin Trung Nguyen, Thai Hoang Le

Published:
17th Mar 2021

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

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

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

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

CONCLUSION: Experimental results on highly challenging data sets (The Extended Cohn-Kanade, FER2013, Oulu-CASIA) show the effectiveness of the technique of combining three models and two proposed models.
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

Published:
17th Mar 2021

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.
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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

Published:
4th Mar 2021

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.
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Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model
Appears in:
phat 21(26): e3

Author:
Prasannavenkatesan Theerthagiri

Published:
3rd Feb 2021

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.
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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

Published:
3rd Feb 2021

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.
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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:
5554

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

Authors:
Manik Sharma, Natalia Romero

Downloads:
1927

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:
1866

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

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
1758

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 »
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:
1709

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 »
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:
1691

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 »
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:
1634

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 »
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:
1544

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 »
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:
1390

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 »
SysML based Design for Variability enabling the Reusability of Legacy Systems towards the support of Diverse Standard Compliant Implementations or Standard Updates: The Case of IEEE-802.15.6 Standard for e-Health Applications
Appears in:
phat 16(5): e1

Authors:
Sofia Meacham, Fotios Gioulekas, Keith Phalp

Downloads:
1322

Abstract:
The aim of this paper is to provide a consistent development path enabling the re-usability of in house legacy systems or architectures towards their re-design, in order to ensure compliance with evolving standards, by using the new features of SysML for modelling variants. Modern standards evolve …The aim of this paper is to provide a consistent development path enabling the re-usability of in house legacy systems or architectures towards their re-design, in order to ensure compliance with evolving standards, by using the new features of SysML for modelling variants. Modern standards evolve quickly, include advanced functionalities and operations and support diverse implementations. System industries need to cope with such standards changes by modifying their current technologies. This paper shows how a novel engineering process (SysML modelling) could be employed to define consistently the specification and the migration procedure of legacy systems to their variants. Within this work SysML characteristics such as package and block diagrams, are employed, with an emphasis on variability modelling, as a basis for standard compliant architecture implementation, thus providing design flexibility and reusability at several abstraction levels. As an illustration of our proposed method we present models of two variant Physical Layer structures for IEEE-802.15.6 Standard for e-Health Applications. The advanced SysML features are used to target the re-usability of a legacy Narrow-Band (NB) physical layer subsystem for the Wireless Body Area Network standard and to implement the alternative Ultra-Wide Band (UWB). Therefore, we contend that such methods bring potential benefits to those needing to ensure compliance when producing product variants.
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Publisher: EAI
ISSN: 2411-7145
Number of Volumes: 7

Last Published: 2021-06-18

EAI Endorsed Transactions on Pervasive Health and Technology

Manik Sharma1

Manik Sharma¹