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: Natalia Romero and Manik Sharma

Guest Editor(s): Sónia Oliveira and Liliana Vale Costa

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)
Vania V. Estrela (Universidade Federal Fluminense, 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)

Natalia Romero¹

Natalia Romero Herrera. Dr. Romero Herrera works as an Assistant Professor at the Faculty of Industrial Design Engineering, Delft University of Technology, in The Netherlands. After obtaining a Computer Engineer title from University of Chile, Dr. Romero Herrera joint a 2 years post-graduate program and conducted her PhD research within the disciplines of Human-Computer Interaction (HCI) and User-Centred Design at Eindhoven University of Technology, in The Netherlands. Currently she has more than 10 years of experience as a design researcher in the area of personal informatics, exploring data-centric interactions that are adopted by people in their daily life. Involved in a dozen of National and European multidisciplinary research projects, Dr. Romero Herrera’s research has contributed to the design and development of in-situ data-centric design methods with the aim to contextualise knowledge and in that way contribute to the design of user-centric innovations. Her design methodology has contributed to the field of Sustainability and Healthcare Living labs. In addition, her research developments has contributed to data-enabled design concepts that aim to empower people in making informed decisions that positively impact quality of life and environmental impact. Her design concepts has contributed to applications areas such as independent living, e- Health and self-management. Dr. Romero Herrera has served as reviewer of a wide number of HCI and design journals, for example TOCHI, JAISE, ACM Transactions, Pervasive Health and Technologies, Behaviour and Technologies, Design studies. She has also participated actively as associate chair as well in the review committee of a range of technology and design conferences. She has recently obtained a national grant to generate contextual knowledge on food behaviour for the creative industry to design innovations in the prevention of overweight, and a European grant to demonstrate the impact of an in-situ data-centric platform as a low-cost energy strategy to stimulate secondary schools across Europe in the reduction of greenhouse emissions and the increase of wellbeing in schools.

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: Delft University of Technology, The Netherlands
2: DAV University, India

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Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model
Appears in:
phat 18: 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.
more »
Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm
Appears in:
phat 18: 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.
more »
Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19
Appears in:
phat 18: e1

Authors:
Chinmay Chakraborty, Arij Naser Abougreen

Published:
28th Jan 2021

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

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

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

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

CONCLUSION: IoT has a significant role in monitoring an individual's health and COVID-19 diagnosis. This paper also highlights the challenges of employing ML and intelligent IoT for fighting COVID-19.
more »

Augmenting Surgery: Medical Students’ Assessment and Ergonomics of 3D Holograms vs. CT Scans for Pre-Operative Planning
Appears in:
phat 21(25): e5

Authors:
Stefano Triberti, Francesco Petrella, Alessandra Gorini, Omar Pappalardo, Valeria Sebri, Lucrezia Savioni, Alberto Redaelli, Gabriella Pravettoni

Published:
8th Jan 2021

Abstract:
INTRODUCTION: Mixed reality (MR) allows surgeons to pre-operatively assess patients’ anatomy (e.g., tissue to be removed). However, medical students have limited access to this technology, and express both the desire to try it and suspicious attitudes. OBJECTIV…INTRODUCTION: Mixed reality (MR) allows surgeons to pre-operatively assess patients’ anatomy (e.g., tissue to be removed). However, medical students have limited access to this technology, and express both the desire to try it and suspicious attitudes.

OBJECTIVES: To assess students’ experience with traditional vs. innovative technology for pre-operative planning.

METHODS: 11 medical students analyzed a lung cancer case using CT scans or a 3D hologram (MR) and assessed the technology in terms of mental workload, emotions and formative value.

RESULTS: MR resulted in less cognitive load and effort, shorter response time and more positive emotions. No differences emerged in formative value, but the students expressed the desire to be trained both in traditional and innovative technology for pre-operative planning.

CONCLUSION: Medical students respond positively to “hands-on” experiences of technology for pre-operative planning. The time may be ripe to include MR in medical formation.
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

Published:
8th Jan 2021

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 »
Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm
Appears in:
phat 21(25): e2

Authors:
Prem Kumar Chandrakar, Akhilesh Kumar Shrivas, Neelam Sahu

Published:
8th Jan 2021

Abstract:
INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers. OBJECTIVES: This paper proposes a Modified Genetic A…INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers.

OBJECTIVES: This paper proposes a Modified Genetic Algorithm (MGA) that is based on Classifier Subset Evaluators – Genetic Search (Eval-CSE_GS) for selecting the relevant feature subsets. The MGA feature selection procedure is applied to microarray information for cancer patients that minimize a high dimension feature subset into low dimension feature subsets.

METHODS: The various data mining methods for classifying the various kinds of cancer disease patients are presented. The proposed model refers to an ensemble model (PEM) for the organization of cancer disease by reducing the feature subsets, which results show improvements in the success rate.

RESULTS: The proposed ensemble model obtains the accuracy of 94.58%, 96.56% and 97.04% for PEM-1 to PEM-3, respectively.

CONCLUSION: Our proposed MGA-PEM model gives satisfactory results for cancer identification and classification.
more »
Using Information and Communication Technologies in Health Teaching and Learning
Appears in:
phat 21(25): e1

Authors:
Sónia Oliveira, Liliana Vale Costa

Published:
8th Jan 2021

Abstract:
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Detection procedures for Patients of Alzheimer’s Disease using Waveform Features of Pupil Light Reflex in response to Chromatic Stimuli
Appears in:
phat 20(24): e6

Authors:
Wioletta Nowak, Minoru Nakayama, Tomasz Kręcicki, Andrzej Hachoł

Published:
16th Dec 2020

Abstract:
INTRODUCTION: Various studies conducted to predict Alzheimer’s disease (AD) indicate that some pupillight reflex (PLR) features may contain symptoms of the disease. An effective procedure that can predict thedisease using PLRs is needed. OBJECTIVES: Two analytic approaches were examined in orde…INTRODUCTION: Various studies conducted to predict Alzheimer’s disease (AD) indicate that some pupillight reflex (PLR) features may contain symptoms of the disease. An effective procedure that can predict thedisease using PLRs is needed.

OBJECTIVES: Two analytic approaches were examined in order to estimate the possibility of identifying Alzheimer’s patients using features of PLR waveforms from chromatic stimuli. In particular, an index of the probability of being an AD patient is introduced, and the features which contributed to PLRs the most were extracted.

METHOD: PLRs for three colours of light pulses (red: 635nm, blue: 470nm, white: CIE x=0.28, y=0.31) at twolevels of intensity (10 and 100 cd/m2) were observed at 60Hz for 10s. Pulses consisted of pre-stimulus (2s), light pulse (1s) and restoration phases (7s). 15 features were extracted from each PLR waveform, such as pupil constriction velocity, pupil response delay, etc. Seven AD patients (age:42-84, mean=68.1) and 12 similar-aged control subjects (age:62-89, mean=72.1).

RESULTS: The first approach was based on factor scores of features of PLRs. Two factor scores were extracted from the 15 features across all measurement conditions, and logistic functions were introduced in order to calculate the probability of identifying AD patients. Function parameters were estimated using a Bayesian technique, such as the Markov chain Monte Carlo method (MCMC). In consideration of the number of participants and biased data distributions, the second approach was based on the sparse modelling technique. Least absolute shrinkage and selection operator (LASSO) was applied to sets of PLR features from each light stimulus, together with the ages of subjects, and optimised result sets were obtained. Prediction performance was higher than with the previous procedure.

CONCLUSION: The use of PLRs features from chromatic stimuli for identifying AD was developed and evaluated.
more »
Risk Assessment of Myocardial Infarction for Diabetics through Multi-Aspects Computing
Appears in:
phat 20(24): e3

Authors:
Shiva Shankar Reddy, Nilambar Sethi, R. Rajender

Published:
16th Dec 2020

Abstract:
INTRODUCTION: Myocardial infarction (MI) is a type of cardiovascular disease. Cardiovascular disease is the major side effect of diabetes. It causes damage to heart muscle due to interruption in the blood flow. The chance of getting this disease is high in diabetes patients. OBJEC…INTRODUCTION: Myocardial infarction (MI) is a type of cardiovascular disease. Cardiovascular disease is the major side effect of diabetes. It causes damage to heart muscle due to interruption in the blood flow. The chance of getting this disease is high in diabetes patients.

OBJECTIVES: To choose a dataset with features related to diabetes, parameters of ECG and risk factors of MI for effective prediction. Predict myocardial infarction in both type-1 and type-2 diabetic patients using regression techniques. Recognise the best algorithm.

METHODS: Multiple linear regression, ridge regression and lasso regression are existing techniques in addition to which proposed technique lasso regression is used to develop a model for prediction. The trained models are compared to know better performing algorithm. Estimation statistics namely confidence and prediction intervals are used to show the amount of uncertainty in predicted values. The statistical measures in regression analysis namely root mean squared error and r_squared value are used to evaluate and compare algorithms.

RESULTS: The proposed algorithm ‘lasso regression’ has achieved better values of RMSE and r_squared as 0.418 and 0.2278 respectively compared to remaining techniques.

CONCLUSION: Best performance of proposed algorithm was noticed and hence using lasso regression for prediction of myocardial infarction in diabetes patients gives better results.
more »
A Multimodal Human Sensing System for Assisted Living
Appears in:
phat 20(24): e5

Authors:
Sonia Sonia, Tushar Semwal

Published:
26th Nov 2020

Abstract:
Introduction: Advancements in sensor technology have resulted in smart systems that can analyze, interpret and understand their surroundings for decision making. Using such intelligent systems for environments such as smart homes, device automation, and hospitals, makes sensing human presence an es…Introduction: Advancements in sensor technology have resulted in smart systems that can analyze, interpret and understand their surroundings for decision making. Using such intelligent systems for environments such as smart homes, device automation, and hospitals, makes sensing human presence an essential requirement. Human sensing becomes critical, especially in assisted living scenarios such as in elderly care. However, mechanisms for human sensing are not foolproof because of the dynamic nature and ability of human beings to deliberately mislead the sensors.

Objective: Objective of paper is to detect human presence in varying environment using non-intrusive sensors.

Method: Besides, sensors have inherent limitations, due to either their mechanism of sensing or the environmental conditions, which can cause them to fail in human detection. These limitations can, at times, cause sensors to provide useless data to the system. In this paper, we propose an adaptive multi-modal human sensing mechanism which can autonomously identify and ignore unnecessary data from a set of sensors, thereby reducing computation complexity, reducing false alarm rate and yielding better performance. The effect of sensing when the human being is in motion has also been studied.

Results: The results portrayed in the paper prove the efficacy of the proposed multi-modal system over itssingle sensor counterparts when used in changing environments and other proposed multi-modal human sensing.

Conclusions: Human sensing is vital to many smart applications such as smart homes, traffic management systems, human-computer interfaces, etc. Since human beings, in general, are always on the move, the use of a dedicated sensor could fail to detect human presence, especially when the ambient parameters around the sensor change. In this paper, a multi-modal human sensing approach has thus been prescribed for overcoming this issue. This work described focused on automating the identification of inappropriate data relayed by some sensors under certain environmental conditions. Experiments reported include both cases - when the sensors are mounted on a static unit (door frame) and also on a mobile robot. The corresponding results reveal that a combination of sensors outperforms the use of individual dedicated sensors for human detection. Analyses of the walking speed of human being have also been studied which endorse the robustness of the approach. However, different directions of motion need exploration in future.
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Diabetes Correlated Renal Fault Prediction through Deep Learning
Appears in:
phat 20(24): e4

Authors:
Shiva Shankar Reddy, Nilambar Sethi, R. Rajender

Published:
11th Nov 2020

Abstract:
INTRODUCTION: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases. OBJECTIVES: The main aim of this work is to develop an effective prediction model using …INTRODUCTION: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases.

OBJECTIVES: The main aim of this work is to develop an effective prediction model using deep learning. To get an effective model, a suitable dataset is considered that comprises of features related to diabetic nephropathy.

METHODS: Deep belief network (DBN) is the proposed deep learning technique which is compared with naive bayes, CART decision tree, logistic regression and support vector machine. DBN is composed of Restricted Boltzmann Machines (RBM). The algorithms are analysed based on evaluation measures like area under PR curve, area under ROC curve, gini coefficient and jaccard index.

RESULTS: After comparison of all algorithms, it was observed that DBN has performed better in terms of AUROC, gini coefficient and jaccard index with values 0.8203, 0.6406 and 0.7777 respectively. But CART obtained better value of 0.9039 only for AUPR.

CONCLUSION: The proposed technique has outperformed other techniques in terms of three metrics and is identified as the best performing algorithm. Hence, it is suggested to use DBN while predicting diabetic nephropathy.
more »
Virtual Learning Approach Toward Introductory Biological Engineering Course in Uruguay During COVID-19
Appears in:
phat 21(25): e3

Authors:
Parag Chatterjee, Andreína Tesis, Camila Simoes, Franco La Paz, María Belén Masset, Lucía Lemes, Vanessa Yelós, Mariano Parodi, Juan Cardelino, Ricardo Armentano

Published:
26th Oct 2020

Abstract:
INTRODUCTION: Global education has seen a paradigm shift in the recent years; especially in life sciences, specializations have started sharing common space in applied research and development. Extending the transdisciplinary approach to undergraduate programs, a case study on the in…INTRODUCTION: Global education has seen a paradigm shift in the recent years; especially in life sciences, specializations have started sharing common space in applied research and development. Extending the transdisciplinary approach to undergraduate programs, a case study on the introductory course of Biological Engineering program at University of the Republic (Universidad de la República) Uruguay is presented.

OBJECTIVES: The COVID-19 pandemic has led to shifting the biological engineering course to virtual modality, changing the pedagogical dynamics. This study aims at analyzing the adaptation to the new model of virtual learning.

METHODS: Different course metrics over time has been analyzed along with surveys on students and professors of the course.

RESULTS: Despite several new challenges posed by the virtual modality, the overall student-performance didn't decline.

CONCLUSION: The biological engineering course presents interesting contents especially in its course design and student engagements, remodeled especially during its virtual mode.
more »
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training
Appears in:
phat 20(24): e2

Authors:
Saravanan. S, Sujitha Juliet

Published:
24th Sep 2020

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

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

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

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

CONCLUSION: Performance metrics are measured in terms of Mean Square Error, Structural similarity Index and PSNR.
more »

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:
3935

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

Authors:
Manik Sharma, Natalia Romero

Downloads:
1748

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

Authors:
Numan Celik, Wamadeva Balachandran, Nadarajah Manivannan

Downloads:
1724

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.
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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:
1571

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

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
1494

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.
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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:
1263

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.
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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:
1253

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.
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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:
1183

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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Reducing alarm fatigue: exploring decision structures, risks, and design
Appears in:
phat 17(10): e2

Authors:
Mustafa Hussain, James Dewey, Nadir Weibel

Downloads:
1118

Abstract:
Automated patient monitoring systems suer from several design problems. Among them, alarm fatigue is one of the most critical issues, as evidenced by the Sentinel Event Alert that The Joint Commission – the U.S. hospital-accrediting body – recently issued. In this study, we explore fast-and-frugal…Automated patient monitoring systems suer from several design problems. Among them, alarm fatigue is one of the most critical issues, as evidenced by the Sentinel Event Alert that The Joint Commission – the U.S. hospital-accrediting body – recently issued. In this study, we explore fast-and-frugal heuristics that may be used to prioritize patient alarms, while continuing to monitor patient physiological state. By using a combination of human factors methodologies and the theory of Distributed Cognition (DCog), we studied alarm fatigue and its relationship to the underlying hospital systems. We identified three specific factors that we envision to be helpful for clinical personnel: ventilator presence, number of intravenous drips, and number of medications. We discuss their application in daily hospital operation. We also address cost-benefit considerations and possible monitor designs.
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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:
1051

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 »

Publisher: EAI
ISSN: 2411-7145
Number of Volumes: 7

Last Published: 2021-01-08

EAI Endorsed Transactions on Pervasive Health and Technology

Natalia Romero1

Manik Sharma2

Natalia Romero¹

Manik Sharma²