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

Aims & Scope
Indexing
Editors in Chief
Editorial Board
Special Issues

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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A Multimodal Human Sensing System for Assisted Living
Appears in:
phat 18: 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 18: e4

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

Published:
11th Nov 2020

Abstract:
INTRODUCTON: 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 …INTRODUCTON: 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.
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Virtual Learning Approach Toward Introductory Biological Engineering Course in Uruguay During COVID-19
Appears in:
phat 18: 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 18: 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.
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Design of Machine Learning and Rule Based Access Control System with Respect to Adaptability and Genuineness of the Requester
Appears in:
phat 18: e1

Authors:
Kriti Srivastava, Narendra Shekokar

Published:
24th Sep 2020

Abstract:
INTRODUCTION: Access control system (ACS) plays a major role in data security. It becomes more challenging for the system to provide accurate ACS, if data is huge and data requesters are not fixed. This is very predominant in the era of big data where new data are adding to the system very frequent…INTRODUCTION: Access control system (ACS) plays a major role in data security. It becomes more challenging for the system to provide accurate ACS, if data is huge and data requesters are not fixed. This is very predominant in the era of big data where new data are adding to the system very frequently. The main issue here is to justify adaptability in ACS.

OBJECTIVE: The objective of this research is to have a comparative analysis of machine learning based access control methods with Rule based access control methods. Propose the most suitable method in detail.

METHODS: Role based access control methods are highly robust and works effectively under known scenarios. We need additional methods to handle unknown scenarios. A decision-making method is used to identify the certainty of the rules and Mamdani fuzzy model is used to evaluate the situation based on current environmental factors. For machine learning based access control method Random Forest is used.

RESULTS: Limitations of machine learning methods are discussed with respect to imbalanced data and bias in the algorithm. The proof of concept for rule-based access control method is tested for all the three modules involved in the framework. Certainty of the rules were accessed with the help of domain experts and accuracy of fuzzy rules were evaluated. Under critical conditions our framework was found to be accurate.

CONCLUSIONS: Machine learning systems are not suitable for access control if they suffer with imbalance data problem. Rule based system are consistent and highly adaptable to unknown situations. Rule based systems have evaluated the genuineness of the requester based on sensitivity of information, time, location, previous history and emergency parameters.
more »

Diagnosis of cardiac arrhythmia using Swarm-intelligence based Metaheuristic Techniques: A comparative analysis
Appears in:
phat 20(23): e7

Authors:
Samriti Sharma, Gurvinder Singh

Published:
22nd Sep 2020

Abstract:
INTRODUCTION: Heart diseases are the prominent human disorders that have significantly affected the lifestyle and lives of the victims. Cardiac arrhythmia (heart arrhythmia) is one of the critical heart disorders that reflects the state ofheartbeat among individual…INTRODUCTION: Heart diseases are the prominent human disorders that have significantly affected the lifestyle and lives of the victims. Cardiac arrhythmia (heart arrhythmia) is one of the critical heart disorders that reflects the state ofheartbeat among individuals. ECG (Electrocardiogram) signals are commonly used in the diagnostic process of this cardiac disorder.

OBJECTIVES: In this manuscript, an effort has been made to employ and examine the performance of emerging Swarm Intelligence (SI) techniques in finding an optimal set of features used for cardiac arrhythmia diagnosis.

METHODS: A standard benchmark UCI dataset set comprises of 279 attributes and 452 instances have been considered. Five different SI-based meta-heuristic techniques viz. binary Grey-Wolf Optimizer (bGWO), Ant Lion Optimization(ALO), Butterfly optimization algorithm (BOA), Dragonfly Algorithm (DA), and Satin-B ird Optimization(SBO) have been also employed for the same. Additionally, five novel chaotic variants of SBO have been designed to solve the feature selection problem for diagnosing a cardiac arrhythmia. Different performance metrics like accuracy, fitness value, optimal set of features and execution time have been computed.

CONCLUSION: It has been observed from the experimentation that in terms of accuracy and fitness value of cardiac arrhythmia, the SBO outperformed other SI algorithms viz. bGWO, DA, BOA, and ALO. Additionally, BOA and ALO seem to be the best fit when the emphasis is on dimension size only.
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Telepediatric assistance in Iran: Specialist and subspecialty challenges
Appears in:
phat 20(23): e6

Authors:
Seyed Masoud Mirmoeini, Seyed Sina Marashi Shooshtari, Gopi Battineni, Francesco Amenta, Seyed Khosrow Tayebati

Published:
22nd Sep 2020

Abstract:
INTRODUCTION: Having justice for easy access to health services may probably be a standard feature and indisputable right of all health policies. The health policy of Iran enunciates this right. Unfortunately, as may happen in many countries, the execution of this policy depends on different…INTRODUCTION: Having justice for easy access to health services may probably be a standard feature and indisputable right of all health policies. The health policy of Iran enunciates this right. Unfortunately, as may happen in many countries, the execution of this policy depends on different factors. Among these parameters should be quoted as the suitable distribution of professionals, hospitals, and medical facilities. On the other hand, in Iran, there are many other problems linked to accessing areas with natural hindrances.

OBJECTIVE: In this study, we analyzed the primary studies that report specialty and subspecialty challenges that had been faced by Iran to improve pediatric assistance.

METHODS: Conduction of literature search in PubMed and CINAHL libraries was done, especially studies from 2010 to 2019. A Boolean operated ‘MeSH’ term was used for search. Newcastle–Ottawa Scale (NOS) scoring was adopted to assess the quality of each study.

RESULTS: A total of 118 studies were displayed, and among them, 102 were excluded due to duplication and study relevance. Study selection was madebased on its content were classified into two groups (1) shortage and unsuitable distribution of specialist and sub-specialist physicians in Iran, and (2) studies that explain the status of degradation in different areas of Iran. Outcomes demonstrated that Iran is generally suffering the shortage and unsuitable distribution of specialists and sub-specialists. This lack is particularly crucial in deprived and far-away areas from the cities.

CONCLUSION: The present study analyzed in detail the current data regarding pediatric health services (specialist and subspecialty) and health-specific facilities distribution in the country.
more »
Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)
Appears in:
phat 20(23): e5

Authors:
Inderpreet Singh Walia, Muskan Srivastava, Deepika Kumar, Mehar Rani, Parth Muthreja, Gaurav Mohadikar

Published:
9th Sep 2020

Abstract:
INTRODUCTION: Pneumonia is most significant disease in today’s world. It resulted around 15 % of the total deaths of children of the same age group. OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning …INTRODUCTION: Pneumonia is most significant disease in today’s world. It resulted around 15 % of the total deaths of children of the same age group.

OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning (VGG16) to reliably diagnose pneumonia.

METHODS: The proposed model contains 10 layers of convolutional neural networks. Also, three dense layers with the Swish activation function with a dropout of 0.7 and 0.5 respectively in each layer. The model was trained on 5216 augmented with weighted contrast and brightened radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16).

RESULT: The model was trained on 5216 augmented radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16) and the final results obtained with training accuracy of 98.5%, testing accuracy of 79.8% and validation accuracy of 75%.

CONCLUSION: The model can be improved by using different transfer learning models and hyperparameter tuning parameters.
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Performance analysis of different machine learning algorithms in breast cancer predictions
Appears in:
phat 20(23): e4

Authors:
Gopi Battineni, Nalini Chintalapudi, Francesco Amenta

Published:
28th Aug 2020

Abstract:
INTRODUCTION: There is a great percentage of failures in clinical trials of early detection of breast cancer. To do this, machine learning (ML) algorithms are useful to do diagnosis and prediction of cancer tumors with better accuracy. OBJECTIVE: In this study, we develop an ML model co…INTRODUCTION: There is a great percentage of failures in clinical trials of early detection of breast cancer. To do this, machine learning (ML) algorithms are useful to do diagnosis and prediction of cancer tumors with better accuracy.

OBJECTIVE: In this study, we develop an ML model coupled with limited features to produce high classification accuracy in tumor classification.

METHODS: We considered a dataset of 569 females diagnosed as 212 malignant and 357 benign types. For model development, three supervised ML algorithms namely support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) were employed. Each model was further validated by 10-fold cross-validation and performance measures were defined to evaluate the model outcomes.

RESULTS: Both SVM and LR models generated 97.66% accuracy with total feature evaluation. With selective features,the SVM accuracy was improved by 98.25%. Whereas the LR model including limited features produced 100% of true positive predictions.

CONCLUSION: The proposed models involved by selective features could improve the prediction accuracy of a breast cancer diagnosis.
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Design of Novel ETL Model to Analyse Corona Virus Data
Appears in:
phat 20(23): e3

Authors:
Amit Kumar Dewangan, S.M. Ghosh, Akhilesh Kumar Shrivas

Published:
22nd Jul 2020

Abstract:
INTRODUCTION: The corona disease was first recognized in 2019 in Wuhan, which is the capital of China’s Hubei-province, and from then it continued spreading and as a result declared as a pandemic by all nations. The COVID-19 virus has different effects on people in various ways. It is a kind of …INTRODUCTION:
The corona disease was first recognized in 2019 in Wuhan, which is the capital of China’s Hubei-province,
and from then it continued spreading and as a result declared as a pandemic by all nations. The COVID-19
virus has different effects on people in various ways. It is a kind of respiratory disease. The confirmed cases
are increasing day to day in India, which leads to complete lockdown throughout the nation.
OBJECTIVE:
The objective of this research is to design a novel Extract-Trandform and Load NETL model to analyse covid19 data in india.
METHODS:
The extraction of useful information from a large database is a well-connected research field of text mining.
This paper is proposed a novel extract-transform-load ETL model to process the COVID-19 data of India to
get the exact recovery data from the multiple data sources from different states of India. In this, a knowledgebased model that generate knowledge based on three different module split, validation, and join is discussed.
RESULTS:
The outcomes of the proposed NETL process are, output file which has the description of total positive cases,
active cases, recovery cases, and death rate, based on different regions. The analysis of NETL is done based
on accuracy, failure count, and execution time. The proposed NETL process is more accurate and taking less
compilation time with minimum failure count as compared with existing models.
CONCLUSION:
To analyze the coronavirus data in India, a novel ETL (NETL) model is proposed. In this model, a total of 9
CSV files is processed as input files to get different results in different categories. This model is having three
modules namely splitting, verification, and join. The dataset is split into based on its coupling attributes and
then joined with a single value to produce the updated results as per the current dataset. The last stage of this
process is to join the data which is generated through splitting. The proposed NETL model is more accurate
as compared with existing ETM models.
more »
Exploring the outdoor performance of a LiDAR-based ground plane checking system for the visually impaired
Appears in:
phat 20(23): e2

Authors:
A.B.C. Chai, B.T. Lau

Published:
22nd Jun 2020

Abstract:
INTRODUCTION: Individuals with visual impairments face a variety of challenges in their daily lives, from daily activities to physical world navigation. One of the biggest challenges is the ability to travel around safely and independently. This challenge is complicated and stressed to the visual…INTRODUCTION: Individuals with visual impairments face a variety of challenges in their daily lives, from daily
activities to physical world navigation. One of the biggest challenges is the ability to travel around safely and
independently. This challenge is complicated and stressed to the visually impaired as the inability to perform obstacles or
ground plane checking will result in severe injury or even death.

OBJECTIVES: This work aims to prove the outdoor performance of the developed solution in detecting and recognising
the frontal ground plane conditions.

METHODS: The proposed model uses a LiDAR module as a distance-measuring tool to perform ground plane checking.

RESULTS: In the selected outdoor path-based scenarios, the ground plane checking system succeeded in achieving an
overall recognition rate of 93.10%, with an overall false positive rate of 2.72% and average false negative rate of 4.25%.

CONCLUSION: Overall, the findings showed the ability of the proposed model to provide effective frontal ground plane
checking for the visually impaired.
more »
Towards Secure, Flexible and Efficient Role Based Hospital’s Cloud Management System: Case Study
Appears in:
phat 20(23): e1

Authors:
Shilpi Harnal, R.K. Chauhan

Published:
22nd Jun 2020

Abstract:
INTRODUCTION: Many organizations of health care have recognized that quality of service can be improved by maintaining e-records of patient’s reports, medical histories, surgeries recordings, etc. over the multimedia cloud servers. But, the data breach is always a matter of stake for an organizat…INTRODUCTION: Many organizations of health care have recognized that quality of service can be improved by
maintaining e-records of patient’s reports, medical histories, surgeries recordings, etc. over the multimedia cloud servers.
But, the data breach is always a matter of stake for an organization as well as for the patients.

OBJECTIVES: This work has considered a qualitative scenario related to multimedia e-content management for a multiforte hospital’s cloud server.

METHODS: An End-to-End Encryption with the cryptographic algorithm is applied along with an access control framework
for secure transmission and storage of e-records.

RESULTS: The results are presented in the form of bar graphs for the time taken to perform encryption/decryption of various
media files and combo graphs for different scenarios of access control.

CONCLUSION: This can provide prevention from many attacks/threats, ensures authentication and privacy along with
limiting the server usage and cost.
more »
A Novel Random Split Point Procedure Using Extremely Randomized (Extra) Trees Ensemble Method for Human Activity Recognition
Appears in:
phat 20(22): e5

Authors:
B Padmaja, V V Rama Prasa, K V N Sunitha

Published:
28th May 2020

Abstract:
INTRODUCTION: Automatic detection and recognition of various human physical movements while performing daily life activities such as walking, jogging, running, sitting, standing etc. are usually considered as Activity Recognition (AR). AR is a prominent research area in many applications, such as e…INTRODUCTION: Automatic detection and recognition of various human physical movements while performing daily life activities such as walking, jogging, running, sitting, standing etc. are usually considered as Activity Recognition (AR). AR is a prominent research area in many applications, such as elderly care, security and surveillance, smart homes, health and fitness. Extremely Randomized Trees Classifier (ET Classifier) is a type of ensemble learning technique used in Activity Recognition, which clusters several different decision trees into a forest from a single learning set and gives the classification result. But it suffers from high variance and over-fitting problem due to high inter-dependency among hyperparameters during model building.

OBJECTIVES: The primary objective of this paper is to propose a novel Random_Split_Point procedure for Extra tree classifier to make the existing approach more robust, less variance, less computational time in obtaining optimal split points and faster in model building. This approach generates K random split points from all the candidate features of the dataset and selects the best split point based on the maximum score obtained by information gain measure.

METHODS: In the proposed method to improve the randomization and accuracy of AR system, a novel random split-point procedure for ET classifier is proposed. This approach reduces the bias-variance problem induced due to the three hyperparameters such as K, nmin and M used in split-point procedure of existing ET classifier (K : number of randomly selected attributes at each node, nmin : minimum sample size for splitting a node, M : number of decision trees for ensemble). This approach generates K random split points from all the candidate features of the dataset and selects the best split point based on the maximum score obtained by information gain measure.

RESULTS: The proposed approach is experimented with two public AR datasets HAR and HAPT (UCI Machine Learning Repository) containing 6 and 12 activities respectively. In HAR dataset, smartphone sensed sensor signals of 3 static and 3 dynamic human daily activities are there, where as in HAPT dataset apart from these 6 daily activities, 6 postural transitions data is available. Experimental results and comparative analysis show that the proposed method outperforms over other existing techniques with an accuracy of 94.16% for HAR dataset and 92.63% for HAPT dataset. It also takes less computational time in finding optimal split-points and less model building time.

CONCLUSION: AR systems can be used as an intelligent system in healthcare to monitor the behaviour of healthy people by recognizing their daily activities. These systems also help in early detection of some chronic diseases and improve the quality of life. In this paper, an attempt is made to improve the accuracy of Activity Recognition over some existing methods.
more »
Corona Virus - SARS-CoV-2: An Insight to Another way of Natural Disaster
Appears in:
phat 20(22): e4

Authors:
Sunil Chawla, Mamta Mittal, Meenakshi Chawla, Lalit Mohan Goyal

Published:
28th May 2020

Abstract:
INTRODUCTION: SARS-CoV-2 is the latest virus responsible for an outburst of a unique respiratory infection identified as COVID-19. The virus popularly known as Corona Virus has spread quickly in recent days from China to several other countries around the world. Health is always of prime concern fo…INTRODUCTION: SARS-CoV-2 is the latest virus responsible for an outburst of a unique respiratory infection identified as COVID-19. The virus popularly known as Corona Virus has spread quickly in recent days from China to several other countries around the world. Health is always of prime concern for mankind. Computing is playing an important role in improving the current state of the healthcare industry.

OBJECTIVES: This paper focuses on summarizing the happenings about the coronavirus and the disease spread. This review study concentrates on the history of the virus, its technical details, the disease caused by the virus, its symptoms and precautions. The study also tries to develop an understanding of the role of technology in dealing with the outbreak, its impact in diverse fields, and the current state of the pandemic.

METHODS: This work is an attempt towards presenting a perspective of computing and technology in fighting against the COVID-19 pandemic. RESULTS: This work presents a perspective showing technology in healthcare as a rescuer in such situations. In this survey, we simply discuss SARS-COV2 and COVID-19 from different perspectives in order to serve as a quick reference for the readers and to achieve a better insight into the fast-evolving pandemic.

CONCLUSION: Social distancing, staying home and lockdowns are some known solutions to combat the pandemic in the absence of the vaccine, and technology can play a significant role in combating the pandemic.
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Support Vector Machine based eHealth Cloud System for Diabetes Classification
Appears in:
phat 20(22): e3

Authors:
Chandrashekhar Azad, Ashok Kumar Mehta, Dindayal Mahto, Dharmveer Kumar Yadav

Published:
20th May 2020

Abstract:
INTRODUCTION: Diabetes is a major health issue because it leaves people with physical disabilities. Therefore, methodologies with a reduced error rate must be used to diagnose this dangerous disease. Data Mining techniques such as Artificial Neural Network are common tools adopted for the classific…INTRODUCTION: Diabetes is a major health issue because it leaves people with physical disabilities. Therefore, methodologies with a reduced error rate must be used to diagnose this dangerous disease. Data Mining techniques such as Artificial Neural Network are common tools adopted for the classification of diabetes and one of the core components of the eHealth system. Data Mining techniques aim to provide reliable and timely diagnostic outcomes during the diagnosis of the disease.

OBJECTIVE: The objective of the research work is to propose a Support Vector Machine based eHealth Cloud System for Diabetes Classification. This work aims to improve the diagnostic accuracy of computer-assisted diagnostic systems.

METHOD: The proposed methodology implemented in two-phase, In the first phase system is trained using different Support Vector Machine (SVM) kernel functions and in the second phase effectiveness of the system is tested in terms of classification accuracy and error. Different SVMs have the ability to diagnose this disease. PIMA Indian Diabetes Dataset (PIDD) has been used in our experiments for training and testing. Kernel functions are usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem.

RESULT: In this classification accuracy and classification error are used for performance evaluation. It is worth mentioning that the system giving remarkable accuracy of 77.50% in Coarse Gaussian SVM in 10-fold validation whereas fine Gaussian SVM gives 98.8% accuracy in No validation set.

CONCLUSION: This paper introduces the SVM eHealth Cloud System for Diabetes Classification. The system is trained using the PIDD. Such a system can be used as “Application-as-a-Service” in cloud computing. It is therefore believed that the system will enhance the process of clinical decision-making and also assist physicians concerning Diabetes Diagnosis. It is worth to mention that the SVM kernel-based system performed well in comparison to the different systems.
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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:
2474

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:
1655

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:
1631

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:
1510

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:
1361

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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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:
1195

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:
1101

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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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:
1095

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

Authors:
Mustafa Hussain, James Dewey, Nadir Weibel

Downloads:
1064

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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A smart phone based gait monitor system
Appears in:
phat 16(7): e1

Authors:
Dong Qin, Ming-Chun Huang

Downloads:
916

Abstract:
Gait is a person’s manner of walking. Analysis of gait can be used in many areas like healthcare, therapy, sports training and characteristic recognition. The goal of this paper is to present a smart phone based system to collect and calculate gait parameters. These parameters which consists of ste…Gait is a person’s manner of walking. Analysis of gait can be used in many areas like healthcare, therapy, sports training and characteristic recognition. The goal of this paper is to present a smart phone based system to collect and calculate gait parameters. These parameters which consists of steps, step length, velocity, cadence, motion intensity and walking regularity were collected by the inertial sensor in the smartphone. A prototype of gait parameter collection and visualization system has been built to collect accelerometer data from the smartphone, providing a reliable algorithm to calculate several gait parameters closely related to walking activity. The system also contains a fall detection function. Once the user suffers from fall, an alarm message will be send to another. Experiment has been done on 4 subjects for testing the stability and accuracy of the system. The experiment result has been compared with the real data. It shows a high accuracy and reliability for counting steps (error<5.47%) and walking duration (error<4.55%). Based on the gait monitor system, an anomaly data detection method is presented. Four independent gait parameters (cadence and motion intensity in three axis) are chosen from previous results during normal activity and their mean and standard deviation are calculated individually. If the latest data deviate from the normal activity model too far, this data is defined as an abnormal event.
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Publisher: EAI
ISSN: 2411-7145
Number of Volumes: 6

Last Published: 2020-09-22

EAI Endorsed Transactions on Pervasive Health and Technology

Natalia Romero1

Manik Sharma2

Natalia Romero¹

Manik Sharma²