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

Call for Papers: Special issue on Data Mining and Knowledge Discovery in Healthcare Analytics

Important dates:

Manuscript submission deadline: 2020-05-20

Notification of acceptance: 2020-06-22

Submission of final revised paper: 2020-07-31

Publication of special issue (tentative): 2020-08-14

Guest editors:

Prof. Saikishor Jangiti (SASTRA Deemed University, India)
Dr. Kulvinder Singh (Kurukshetra University, India)
Dr. Rohit Tanwar (School of Computer Sciences, UPES, Dehradun, India)

Call for Papers: Special issue on Using Information and Communication Technologies in Health Teaching and Learning

Important dates:

Manuscript submission deadline: 2020-09-19

Notification of acceptance: 2020-10-30

Submission of final revised paper: 2020-11-30

Publication of special issue (tentative): 2020-12-18

Guest editors:

Sonia Oliveira (University of Aveiro, Portugal)
Liliana Vale Costa (DIGIMEDIA Research Centre, University of Aveiro)

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)
Heiko Mueller (OFFIS, Germany)
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)

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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Exploring the outdoor performance of a LiDAR-based ground plane checking system for the visually impaired
Appears in:
phat 18: 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 18: 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.
more »
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.
more »
Effects of Memory Dependent Derivative of Bio-heat Model in Skin Tissue exposed to Laser Radiation
Appears in:
phat 20(22): e2

Authors:
Iqbal Kaur, Parveen Lata, K. S. Handa

Published:
19th May 2020

Abstract:
INTRODUCTION: Thermal processes are the essence of living organisms and are necessary for understanding life. The study of Transfer of heat in tissues is known as Bioheat transfer. Many techniques are developed for the thermal treatment of skin and other disease such as skin cancer, skin burns and …INTRODUCTION: Thermal processes are the essence of living organisms and are necessary for understanding life. The study of Transfer of heat in tissues is known as Bioheat transfer. Many techniques are developed for the thermal treatment of skin and other disease such as skin cancer, skin burns and injured skin tissue with laser. The tissue is inhomogeneous and at times anisotropic with complex thermal properties. Moreover, there may be skin tissue damage when irradiated with a laser beam.

OBJECTIVES: In this research a novel one-dimensional (1-D) bioheat model has been used with memory-dependent derivative (MDD) in Pennes’ bioheat transfer equation due to laser radiation and the thermal damage in tissue caused due to laser heating has been examined.

METHODS: Bioheat transfer model has been used with memory-dependent derivative (MDD) in Pennes’ bioheat transfer equation. The problem is solved using Laplace transform technique.

RESULTS: The temperature and thermal damage in the skin exposed to heating with laser radiation is calculated and obtained in physical form. The thermal reaction of skin tissues during laser radiation is studied under memory-dependent derivative (MDD) in Pennes’ bioheat transfer equation.

CONCLUSION: Analyzed a novel bioheat mathematical model on the basis of MDD involving time-delay parameter 𝜒𝜒 for the Pennes’ bioheat transfer equation and applied to examine the thermal properties of the skin tissue for burns caused due to laser radiations. The thermal damages can be measured in a better way with the MDD model. The blood perfusion prevent the tissue damage by developing the cooling function. Effect of memory dependent derivatives and time delay parameter are represented graphically and analysed.
more »
Early Detection of Diabetic Eye Disease through Deep Learning using Fundus Images
Appears in:
phat 20(22): e1

Authors:
Rubina Sarki, Khandakar Ahmed, Yanchun Zhang

Published:
19th May 2020

Abstract:
INTRODUCTION: Diabetic eye disease (DED) is a group of eye problems that can aﬀect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Diabetes can damage your eyes over time, which can lead to poor vision or even permanent blindness. Early…INTRODUCTION: Diabetic eye disease (DED) is a group of eye problems that can aﬀect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Diabetes can damage your eyes over time, which can lead to poor vision or even permanent blindness. Early detection of DED symptoms is therefore essential to prevent escalation of the disease and timely treatment. Research diﬃculties in early detection of DEDs can so far be summarized as follows: changes in the eye anatomy during its early stage are often untraceable by the human eye due to the subtle nature of the features, where large volumes of fundus images put tremendous pressure on scarce specialist resources, making manual analysis practically impossible.

OBJECTIVES: Therefore, methods focused on deep learning have been practiced to promote early detection of DEDs and address the issues currently faced. Despite promising, highly accurate identiﬁcation of early anatomical changes in the eye using Deep Learning remains a challenge in wide-scale practical application.

METHODS: We present conceptual system architecture with pre-trained Convolutional Neural Network combined with image processing techniques to construct an early DED detection system. The data was collected from various publicly available sources, such as Kaggle, Messidor, RIGA, and HEI-MED. The analysis was presented with 13 Convolutional Neural Networks models, trained and tested on a wide-scale imagenet dataset using the Transfer Learning concept. Numerous techniques for improving performance were discussed, such as (i) image processing,(ii) ﬁne-tuning, (iii) volume increase in data. The parameters were recorded against the default Accuracy metric for the test dataset.

RESULTS: After the extensive study about the various classiﬁcation system, and its methods, we found that creating an eﬃcient neural network classiﬁer demands careful consideration of both the network architecture and the data input. Hence, image processing plays a signiﬁcant role to develop high accuracy diabetic eye disease classiﬁers.

CONCLUSION: This article recognized speciﬁc work limitations in the early classiﬁcation of diabetic eye disease. First, early-stage classiﬁcation of DED, and second, classiﬁcation of DR, GL, and DME using a method that causes permanent blindness afterward. Lastly, this study was intended to propose the framework for early automatic DED detection of fundus images through deep learning addressing three main research gaps.
more »
Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study
Appears in:
phat 20(21): e5

Authors:
Bijoy Chhetri, Lalit Mohan Goyal, Mamta Mittal, Sandeep Gurung

Published:
31st Jan 2020

Abstract:
Background: A menace case of drug & narcotics abuse has been in prime focus of the society nowadays. Therefore, the need of technological intervention is primary concern to examine the prevalence, severity and outcome to the drug menace and its consequences. Objective: This study is to suffice …Background: A menace case of drug & narcotics abuse has been in prime focus of the society nowadays. Therefore, the need of technological intervention is primary concern to examine the prevalence, severity and outcome to the drug menace and its consequences.

Objective: This study is to suffice clinical decisions through behaviour observatory data through preliminary screening of prevalence, correlation and severity of illness.

Method: The model has been proposed to check for General Anxiety Disorder and Depression of a subject abusing any of the drug/marijuana/alcohol. In this model data set of Sikkim’s youth has been considered to find relation of addiction leading to mental disorder.

Result: This proposed system has been successful to associate any form of substance abuse to to some of illness to a limit of .83 accuracy scored by Support Vector Machine over the other machine learning models. The model has been deployed and being observed in few of the rehabilitation centre.
more »
Enhancing Intrinsic Motivation in Physical Activity through Quantified-self Data Sharing
Appears in:
phat 20(21): e4

Authors:
Nan Yang, Gerbrand van Hout, Loe Feijs, Wei Chen, Jun Hu

Published:
31st Jan 2020

Abstract:
INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of …INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling.

OBJECTIVES: To design and evaluate SocialBike - a digitally augmented bicycle that aims to increase user’s motivation in physical activity by showing their quantified-self data to each other.

METHODS: We conducted a within-subject experiment on a cycling simulation system with 20 participants. The on-bike display of SocialBike was compared to a control display to evaluate its impact on two dependent variables, including user’s intrinsic motivation in physical activity and social relatedness with other cyclists nearby. Intrinsic Motivation Inventory (IMI) was used to measure users’ self-reported intrinsic motivation and social relatedness. The user’s cycling behaviour data were also recorded by the simulation system. We extracted six indicators about the two dependent variables from the behaviour data. Qualitative data were collected through semi-structured interviews. We conducted a paired sample T-test on both types of quantitative data. Qualitative data were analysed by the method of thematic analysis.

RESULTS: The results of the Intrinsic Motivation Inventory show that SocialBike’s on-bike display has significant positive effects on four subscales, including intrinsic motivation, perceived competence, values/usefulness, and relatedness. The results of user cycling behaviour indicate that SocialBike increased user’s social relatedness with other cyclists nearby by significantly affecting three indicators. Three themes were identified from qualitative data, including motivation, social interaction, and data visualization. The qualitative result provides supplementary evidence for the quantitative result and additional insights into the overall design of SocialBike.

CONCLUSION: SocialBike as a platform for sharing quantified-self data have positive effects in enhancing users’ intrinsic motivation in physical activity and social relatedness with other cyclists nearby.
more »
Conceptual framework of Digital Health Public Emergency System: digital twins and multiparadigm simulation
Appears in:
phat 20(21): e3

Authors:
Román Rodríguez-Aguilar, Jose-Antonio Marmolejo-Saucedo

Published:
31st Jan 2020

Abstract:
INTRODUCTION: Two major technological paradigms have been developed in recent years, digital twins and the multiparadigm simulation. In the Health Sector, the enormous potential of both approaches for the management of public health emergencies is envisioned. OBJECTIVES: This study aims to deve…INTRODUCTION: Two major technological paradigms have been developed in recent years, digital twins and the multiparadigm simulation. In the Health Sector, the enormous potential of both approaches for the management of public health emergencies is envisioned.

OBJECTIVES: This study aims to develop the conceptual framework for the development of a Digital Public Health Emergency System.

METHODS: The integration of the digital twins in health with the multi-paradigm simulation for the design of a digital system of public health emergencies is proposed.

RESULTS: The proposal establishes the conceptual framework for the integration of a digital public health emergency system, incorporating multi-paradigm simulation in the construction of a digital twin.

CONCLUSION: The use of cutting-edge technologies for public health emergencies will allow for a better response to contingencies as well as optimally managing the resources available in a health system.
more »
Designing a resilient supply chain: An approach to reduce drug shortages in epidemic outbreaks
Appears in:
phat 20(21): e2

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

Published:
31st Jan 2020

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

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

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

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

CONCLUSION: Drug providers should consider strategies to prevent or reduce the impact of shortages as well as disruption spreads.
more »
Multimodal Deep Learning based Framework for Detecting Depression and Suicidal Behaviour by Affective Analysis of Social Media Posts
Appears in:
phat 20(21): e1

Authors:
Anshu Malhotra, Rajni Jindal

Published:
31st Jan 2020

Abstract:
INTRODUCTION AND OBJECTIVES: Currently, no social media platform has deployed a real time system which can analyse users’ state of mind based on day to day posts on continual basis and detect the onset of depression, suicidal or self harming behaviour etc. Platforms majorly rely on manual reporting…INTRODUCTION AND OBJECTIVES: Currently, no social media platform has deployed a real time system which can analyse users’ state of mind based on day to day posts on continual basis and detect the onset of depression, suicidal or self harming behaviour etc. Platforms majorly rely on manual reporting of suicidal and self harming behavior. In this paper, we propose a real time, deep learning based system for aﬀective analysis of a user’s online social media posts of multimodal nature, with the objective of detecting onset of depression and suicidal or self harming behaviour; as depression often drives people to commit suicide or harm themselves physically.

METHODS: Joint representations are obtained by fusing the individual vector representations of multiple modalities from user’s social media feed: text, image and videos. These vector representations are in turn obtained through state of the art approaches for each modality e.g. VGG-16 for feature extraction from images, word2vec for text and Faster R-CNN on video frames. These joint representations are used to obtain weighted average score which can be used for making the ﬁnal classiﬁcation using the Softmax prediction layer.

SIGNIFICANCE AND IMPACT: To the best of our knowledge, this is the ﬁrst research where the use of deep learning techniques has been proposed for real time detection of onset of depression and suicidal behaviour by analysing multimodal user generated content.
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:
1644

Abstract:
Existing research methods are largely intended to be conducted co-located and synchronously with a study population, but this approach is not feasible with remote or distributed populations. We describe a needs assessment study we conducted on Facebook. We report on our use of adapted versions of c…Existing research methods are largely intended to be conducted co-located and synchronously with a study population, but this approach is not feasible with remote or distributed populations. We describe a needs assessment study we conducted on Facebook. We report on our use of adapted versions of commonly used HCI research methods and lessons learned from this approach.
more »
Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis
Appears in:
phat 18(15): e1

Authors:
Manik Sharma, Natalia Romero

Downloads:
1491

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:
1489

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:
1394

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:
1143

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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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:
999

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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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:
996

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

Authors:
Mustafa Hussain, James Dewey, Nadir Weibel

Downloads:
936

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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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:
873

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

Authors:
Dong Qin, Ming-Chun Huang

Downloads:
843

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: 2020-05-28 09:35:55 UTC

EAI Endorsed Transactions on Pervasive Health and Technology

Natalia Romero1

Manik Sharma2

Natalia Romero¹

Manik Sharma²