It is internationally recognized that future healthcare systems will be supported by pervasive healthcare systems in order to target the upcoming societal health challenges. Pervasive Health and Technology (PHT) Journal is a cross-disciplinary journal focusing on issues related to development, delivery and management of pervasive healthcare and the impact of technology within future healthcare systems.

The journal aims to become an information source and to create a community of shared interests between health professionals, technologists, patients and other professionals related to pervasive healthcare.

The journal will present and discuss issues pertaining to 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, implementation and evaluation of supporting hardware and software in ambulant and hospital workflows, needs of physicians, nurses and other healthcare staff.

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: eHealth cloud services (Submission of manuscript: 2019-11-29; First notification: 2020-01-06; Final notification: 2020-02-10; Publication date: 2020-03-30)

Guest editors: Prof. Saikishor Jangiti (School of Computing, SASTRA Deemed University, Thanjavur, India)

Call for Papers: Special issue on: Smart IOT and Edge/Fog Computing for Connected Healthcare (Manuscript submission deadline: 2020-03-16; Notification of acceptance: 2020-04-20; Submission of final revised paper: 2020-05-22; Publication of special issue (tentative): 2020-06-30)

Guest editors: Dr. Jothi K R (Vellore Institute of Technology, India)

Dr. G. R. Kanagachidambaresan (Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, India)

Dr. Madhavan P (SRM Institute of Science and Technology, India)

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)

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

Recently Published
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AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction
Appears in:
phat 19(17): e5

Authors:
Ansh Mittal, Deepika Kumar

Published:
12th Feb 2019

Abstract:
INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis. OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (…INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis.

OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis results.

METHODS: AiCNNs model integrates 5 already trained models including simple convolutional neural networks (one uses a simple CNN while the other utilizes data augmentation) and three pre-trained networks whose weights are transferred from ImageNet dataset.

RESULTS: AiCNNs model was trained on 3501 augmented T1-weighted contrast enhanced MRI (CE-MRI) brain images. Validation results of 99.49% (loss=0.0303) had been achieved by AiCNNs on a set of 1167 images, which outperform its contemporaries which have got results upto 97.81% (loss=0.1794) and 97.79% (loss=0.1787).

CONCLUSION: AiCNNs has been shown to obtained a test accuracy of 98.89 % on a set of 1167 images
more »
Analysis for Extracted Features of Pupil Light Reflex to Chromatic Stimuli in Alzheimer’s Patients
Appears in:
phat 19(17): e4

Authors:
Wioletta Nowak, Minoru Nakayama, Tomasz Kręcicki, Elzbieta Trypka, Artur Andrzejak, Andrzej Hachoł

Published:
12th Feb 2019

Abstract:
INTRODUCTION: Some Alzheimer’s Disease (AD) patients respond to chromatic light stimulus, which may influence intrinsically photosensitive retinal ganglion cells (ipRGCs), due to factors common to both AD and Age-Related Macular Disease (AMD). OBJECTIVES: In this study, short light pulses of t…INTRODUCTION: Some Alzheimer’s Disease (AD) patients respond to chromatic light stimulus, which may
influence intrinsically photosensitive retinal ganglion cells (ipRGCs), due to factors common to both AD and
Age-Related Macular Disease (AMD).

OBJECTIVES: In this study, short light pulses of three colours were introduced to a novel diagnostic procesure for AD patients such as classification techniques using waveform features of pupil light reflexs (PLRs), and their prediction performances were evaluated.

METHOD: PLRs to 1s pulses of red, blue and white stimuli shown at high and low photopic levels followed
by a 7s restoration process were recorded after the stimuli were shown to 7 AD patients and 12 non-AD
participants (aged 42-89). Features of waveform shapes of PLRs in 5 dimensions and 15 features of PLRs were extracted.

RESULTS: In a classification analysis, most non-AD participants were correctly identified using the same
level of performance we reported when PLRs for red and blue stimuli were used to measure the performance
of AD patients. There were significant differences in some of the features of PLRs extracted from the two
groups (AD and non-AD participants), particularly with the features for blue light stimuli in high brightness,
which produced significant reactions in AD patients. The classification performance of using 15 features of
the response to blue light stimuli was the highest among responses for all three colours, and was higher than the performance using the procedure in the previous study. Also, a few of the features extracted using the three colours of stimuli changed significantly across age ranges (70 and under, 71-80, and over 80), so these may indicate factors related to ageing.

CONCLUSION: These results confirm that some specific features of PLRs, in particular the response to blue light, can indicate the existence of AD in patients. Also, a few of the features may reveal factors related to
ageing during evaluations which use PLRs. This evidence may help in the better understanding of features of PLRs.
more »
Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network
Appears in:
phat 19(17): e3

Authors:
Netzahualcoyotl Hernandez, Chris Nugent, Ian McChesney, Shuai Zhang, Jesus Favela

Published:
12th Feb 2019

Abstract:
INTRODUCTION: Neural networks are a popular type of algorithm for human activity monitoring which can build intelligent systems from labelled data in an automated fashion. Obtaining accurately labelled data is costly; it requires time and effort, which can be cumbersome because it interrupts the u…INTRODUCTION: Neural networks are a popular type of algorithm for human activity monitoring which can
build intelligent systems from labelled data in an automated fashion. Obtaining accurately labelled data is costly; it requires time and effort, which can be cumbersome because it interrupts the user activity stream. In conjunction with the ubiquitous presence of embedded technology, neural networks present new research opportunities for human activity monitoring in smart home environments.

OBJECTIVES: We propose a human activity classification method that requires a limited amount of
labelled data, which consists of a concatenation method for classifying human activities built upon the fusion of neural network activation functions.

METHODS: Our methodology builds a neural network model that receives the sensor data through the input
layer to then distribute it among the different vertical hidden layers, which implement different activation
functions simultaneously. Next a hidden layer combines activation functions by utilising a concatenation
method. Finally, the neural network provides classes to the unlabelled sensing data. We conducted an
evaluation utilising an open-access dataset. We compared the activity recognition accuracy of our approach utilising 25%, 50%, and 75% of labelled data against a conventional shallow neural network trained with the 100% of labelled data available.

RESULTS: Results show an improvement in the accuracy of the activity classification regardless of the portion of labelled data available. It was observed that the highest achieved accuracy when using 25% of activation function fusion data outperformed results compared to when using 100% of labelled data in a conventional shallow network (i.e., increase in accuracy of 2.7%, 3.7%, 4.8%, and 0.9% across the activity recognition of four subjects).

CONCLUSION: The approach proposed showed an improvement in the accuracy of classifying human
activity when a limited amount of labelled data is available.
more »
A review of youth mental health promotion apps towards their fit with youth media preferences
Appears in:
phat 19(17): e2

Authors:
T. Michel, F. Tachtler, P. Slovak, G. Fitzpatrick

Published:
12th Feb 2019

Abstract:
INTRODUCTION: Mental health promotion apps can promote youth mental health but fail to engage young people. Fit to young people’s media preferences is known to mediate engagement. OBJECTIVES: To explore the fit of existing youth mental health apps with young people’s media preferences. METH…INTRODUCTION: Mental health promotion apps can promote youth mental health but fail to engage young
people. Fit to young people’s media preferences is known to mediate engagement.

OBJECTIVES: To explore the fit of existing youth mental health apps with young people’s media
preferences.

METHODS: A workshop with 60 youth psychologists elicits designs of digital mental health interventions. A
review of 29 youth mental health apps unpacks their modality strategies. We then compare modality strategies from literature on youth mental health, media preferences and engagement, and from the experts, with strategies in current mental health apps to identify potential fit problems.

RESULTS: There is a mismatch between young people’s modality preferences and how youth mental health apps deliver their content.

CONCLUSION: There is a need to make youth mental health promotion apps more interactive and tailorable,
featuring dynamic visuals and social connectivity, to better engage youth.
more »
Paradoxes in Eye Health Care in Low-Income Countries and Design Strategies for Mobile Health Technology to Overcome them
Appears in:
phat 19(17): e1

Authors:
Esmael Kedir Nida, Luc Geurts, Vero Vanden Abeele

Published:
12th Feb 2019

Abstract:
INTRODUCTION: Low income countries bear 90% of the burden of avoidable blindness. In particular, glaucoma is one of the major causes of visual impairment. Early detection is critical to avoid irreversible blindness due to this underdiagnosed disease. Mobile and connected technologies yield potenti…INTRODUCTION: Low income countries bear 90% of the burden of avoidable blindness. In particular, glaucoma is one of the major causes of visual impairment. Early detection is critical to avoid irreversible blindness due to this underdiagnosed
disease. Mobile and connected technologies yield potential to support early detection. However, to realize this potential, they should be designed from a deep understanding of the needs of the local population, and the challenges unique to lowincome countries.

OBJECTIVES: To gain a contextualized understanding of eye health care needs, and the challenges and opportunities for designing mobile health solutions for screening of glaucoma.

METHODS: We conducted a qualitative user study at three different healthcare centres in Southwest Ethiopia, including semi-structured interviews with eight eye care health professionals and observations and informal interviews with 125 patients, at three different locations. Interview transcripts were analysed through inductive thematic analysis involving three iterations.

RESULTS: Seven themes were initially identified after two iterations. The themes provided insight into the organization of eye health care and highlighted the challenges faced by eye care professionals and patients. An additional of three themes were identified in the third iteration which were formulated as paradoxes (“Money is not an issue, yet there is no money for glaucoma”, “Shortage of eye health care, yet the work is repeated over and over”, “The willing but uncooperative patient tested by the willing but unexperienced nurse”).

CONCLUSION: Findings from this research bear the potential to inspire future designs of mobile health solutions targeting rural communities in Ethiopia and other LICs.
more »
Design and Implementation of Data Collection & Analysis Tool for Healthcare Parameter Monitoring using Inverse Low Pass Filter
Appears in:
phat 18(16): e5

Authors:
S. K. Chaurasia, S.R.N Reddy

Published:
30th Oct 2018

Abstract:
BACKGROUND: Health of an individual can be determined by monitoring his daily activities. Human Activity Recognition (HAR) is a process, used to record these activities. The first step of HAR involves collection of Data from various Sensors, which is challenging due to the requirement of interfacin…BACKGROUND: Health of an individual can be determined by monitoring his daily activities. Human Activity Recognition (HAR) is a process, used to record these activities. The first step of HAR involves collection of Data from various Sensors, which is challenging due to the requirement of interfacing of hardware Sensors. Researchers either do hardware interfacing themselves or just analyse already available dataset. In the literature, many measures are proposed to calculate the human activities by using the Accelerometer data. But, different activities recorded by Accelerometer are not accurate due to presence of gravity component. Also there is requirement of identification of appropriate Machine learning algorithm for HAR.

OBJECTIVE: To facilitate the researchers in hassle free data collection for monitoring HAR and finding optimal cutoff frequency to remove gravity component present in accelerometer data. Also detecting suitable machine learning model for HAR.

METHODS: An application has been developed using Inverse low pass filter with an optimal threshold value to compute cut-off frequency for the removal of gravity component from accelerometer data. 8 different machine learning models are trained and tested for examining suitability of models on the collected data at server. Accuracy and execution time are considered as validation parameters.

RESULTS: Gravity Component is removed and data gathered by using 0.6 alpha is providing 0.05 mean acceleration in static state. Among Various ML models k Nearest Neighbor is providing 93.17 percent of accuracy with 0.187486 sec of execution time.

CONCLUSION: Removal of Gravity component is optimised by our method and out of various ML model kNN is identified as suitable model for HAR.
more »
Epidemiology of Breast Cancer in Indian Women: Population and Hospital Based study
Appears in:
phat 18(16): e4

Authors:
Deepika Kumar, Usha Batra

Published:
30th Oct 2018

Abstract:
BACKGROUND: Breast Cancer is a leading cause of mortality and ranked apex among females all over the world. Early detection and diagnosis of breast cancer may reduce the increasing burden of the disease. With this background, a Population and Hospital based cancer registries have been studied to un…BACKGROUND: Breast Cancer is a leading cause of mortality and ranked apex among females all over the world. Early detection and diagnosis of breast cancer may reduce the increasing burden of the disease. With this background, a Population and Hospital based cancer registries have been studied to understand the epidemiology of breast cancer trends in India.

OBJECTIVE: The objective of this study is to gather information provided in different cancer registries and analyze the epidemiology of breast cancer statistics in India.

METHODS: Age Adjusted Rate, Age Specific Rate and Mortality Incidence Ratio has been considered as evaluation metric.

RESULTS: In this study it has been observed that the Age Adjusted Rate and Mortality rate of Breast Cancer has been reported as 25.8 and 12.7 per 100,000 women respectively.

CONCLUSION: Additional awareness & screening programs early diagnosis and treatment facilities can only significantly improve the breast cancer’s clinical picture in India.
more »
CareD: Non-Pharmacological Assistance for Dementia Patients
Appears in:
phat 18(16): e3

Authors:
Kainat Siddiq, Narmeen Bawany, Tehreem Qamar, Ayesha Naveed, Bisma Arif, Mariam Usman, Mehek Jamal

Published:
30th Oct 2018

Abstract:
BACKGROUND: Dementia is a common form of Alzheimer disease which is affecting the quality of older adults’ life. Non-drug treatment such as cognitive therapies are more beneficial for such patients. These days, mobile applications are providing tremendous opportunities for aiding the medicinal trea…BACKGROUND: Dementia is a common form of Alzheimer disease which is affecting the quality of older adults’ life. Non-drug treatment such as cognitive therapies are more beneficial for such patients. These days, mobile applications are providing tremendous opportunities for aiding the medicinal treatment.

OBJECTIVES: This paper presents CareD – a mobile application based solution for dementia patients and their caregivers. Unlike, other available mobile applications, CareD provide a single resolution for maximum dementia related complications. The main objective of this application is to improve the quality of life of dementia patients and facilitate their caregivers. CareD also aims at providing native language support i.e. Urdu to the people of Pakistan.

METHODS: CareD comprises of various modules including cognitive therapy sessions, patient tracking, reminders, healthy living to aid dementia patients in their everyday activities. Experimental study involving dementia patients and their caregivers has been carried out to evaluate the usability and effectiveness of this application.

RESULTS: Twenty patients were included in the study. Results showed that CareD is very effective and helpful in decreasing the disease progression as well as in relieving the burden of caregivers.

CONCLUSION: CareD has been found to be a useful and efficient solution for dementia patients. It was observed that utilization of mobile application was progressively successful in reducing the stress of both; patients and their caregivers. The use of native language Urdu made the application more learnable for older adults and improved its acceptance.
more »
Opinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule
Appears in:
phat 18(16): e2

Authors:
Mamta Mittal, Iqbaldeep Kaur, Subhash Chandra Pandey, Amit Verma, Lalit Mohan Goyal

Published:
30th Oct 2018

Abstract:
Communication among several internet users has become more convenient through social networking sites to where each user sharing his own opinions on different matters, such as Healthcare, Education, marketing etc. The Objective of this paper is to present a method to make it easier for even a layma…Communication among several internet users has become more convenient through social networking sites to where each user sharing his own opinions on different matters, such as Healthcare, Education, marketing etc. The Objective of this paper is to present a method to make it easier for even a layman to predict and analyze one’s health issues on his own by
making use of tweets on the social website twitter.com. As far as methodology or techniques is concerned, an algorithm has been framed for the same to perform the analysis on health care tweets with association rules to classify the ailments and their symptoms using a corpus through fuzzy set and two step approach for Document Term Matrix & Term Document Matrix. The results demonstrate the comparison of different terms over the WordCloud which concludes that in
this novel approach of two step authentication the average accuracy of association between the hiv ailments is 98% through correlation table and association between the HIV ailments with 98% correlation.
more »
Designing human data interactions in Healthcare
Appears in:
phat 18(16): e1

Authors:
Natalia Romero, Manik Sharma

Published:
30th Oct 2018

Abstract:
more »

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

Authors:
Manik Sharma, Natalia Romero

Downloads:
1285

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:
1257

Abstract:
In this paper, we present a multi-parameter wearable sensor system in conjunction with a smartphone to enable a real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical research illustrating that…In this paper, we present a multi-parameter wearable sensor system in conjunction with a smartphone to enable a real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical research illustrating that continuing accurate measurements of core body temperature (CBT) are crucial to investigate human thermoregulation in ambulatory environment and during activity. On the other hand, ECG remains the mainstay test for primary diagnosis and survival analysis of heart diseases. We implement a wireless multisensory system that measures both the tympanic temperature inside the ear and ECG data from an ear and an arm. The behind-the-Ear device makes desirable to measure ECG data without any chest belt and allow the patient to move around freely. The proposed Bluetooth device has similarity of a hearing aid and is wirelessly connected to a smartphone for data transmission and displaying. This device not only gives access to the core temperature and ECG data in real time, but also the device can be controlled – removed and reapplied – by the patient at any time, thus increasing the performance of personal health applications.
more »
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:
1182

Abstract:
The power transfer efficiency of energy harvesting systems is strongly dependent on the power conditioning circuits, especially rectifiers. The voltage drop across rectifier and its leakage current can drastically influence the efficiency. The hybrid energy harvesters impose even more severe co…The power transfer efficiency of energy harvesting systems
is strongly dependent on the power conditioning circuits,
especially rectifiers. The voltage drop across rectifier and
its leakage current can drastically influence the efficiency.
The hybrid energy harvesters impose even more severe constraints on the rectifier. The low Vth transistors and bulk
regulation technique are used in this work to mitigated the
voltage drop and leakage current, respectively. It has been
shown that the bulk regulation stops the current leakage
through body of PMOS transistor. A near zero threshold
cross connected CMOS rectifier is presented in this work using the standard 180nm UMC technology and experimental
analysis are carried out to evaluate the circuit performance.
more »
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:
1168

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 »
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:
822

Abstract:
Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems. In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to the adjacent body joints. To deriv…Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems.
In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to
the adjacent body joints. To derive an efficient, robust and precise method based on a practical procedure is a crucial as well as
challenging task when developing a wearable system with multiple
embedded IMUs. In this work, first, we perform a theoretical analysis of an existing position calibration method, showing its limited
applicability for the hip and knee joint. Based on this, we propose a method for simultaneously estimating the positions of three IMUs
(mounted on pelvis, upper leg, lower leg) relative to these joints.
The latter are here considered as an ensemble. Finally, we perform
an experimental evaluation based on simulated and real data, showing the improvements of our calibration method as well as lines of future work.
more »
SysML based Design for Variability enabling the Reusability of Legacy Systems towards the support of Diverse Standard Compliant Implementations or Standard Updates: The Case of IEEE-802.15.6 Standard for e-Health Applications
Appears in:
phat 16(5): e1

Authors:
Sofia Meacham, Fotios Gioulekas, Keith Phalp

Downloads:
798

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

Authors:
Mustafa Hussain, James Dewey, Nadir Weibel

Downloads:
752

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

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
735

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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Mining Minds: an innovative framework for personalized health and wellness support
Appears in:
phat 15(2): e5

Authors:
Oresti Banos, Muhammad Amin, Wahajat Khan, Taqdir Ali, Muhammad Afzal, Byeong Kang, Sungyoung Lee

Downloads:
685

Abstract:
The world is witnessing a spectacular shift in the delivery of health and wellness care. The key ingredient of this transformation consists in the use of revolutionary digital technologies to empower people in their self-management as well as to enhance traditional care procedures. While substantia…The world is witnessing a spectacular shift in the delivery of health and wellness care. The key ingredient of this transformation consists in the use of revolutionary digital technologies to empower people in their self-management as well as to enhance traditional care procedures. While substantial domain-specific contributions have been provided to that end in the recent years, there is a clear lack of platforms that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these technologies. This work presents Mining Minds, an innovative framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized healthcare and wellness support. Mining Minds embraces some of the currently most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, and state-of-the-art concepts and methods, such as Context-Awareness, Knowledge Bases or Analytics, among others. This paper aims at thoroughly describing the efficient and rational combination and interoperation of these modern technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support.
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A smart phone based gait monitor system
Appears in:
phat 16(7): e1

Authors:
Dong Qin, Ming-Chun Huang

Downloads:
668

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: 5

Last Published: 12th Feb 2019

EAI Endorsed Transactions on Pervasive Health and Technology

Natalia Romero1

Manik Sharma2

Natalia Romero¹

Manik Sharma²