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: Pervasive health, chronic diseases and new paradigms in health care (Manuscript submission deadline: 2020-02-28; Notification of acceptance: 2020-03-30; Submission of final revised paper: 2020-04-30; Publication of special issue (tentative): 2020-05-15)

Guest editors: Prof. Roman Rodriguez-Aguilar (Universidad Panamericana, Mexico)

Prof. Felix Martinez-Rios (Universidad Panamericana, Mexico)

Prof. Jose Antonio Marmolejo-Saucedo (Universidad Panamericana, Mexico)

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

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Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study
Appears in:
phat 18: e2

Authors:
Iqra Nissar, Danish Raza Rizvi, Sarfaraz Masood, Aqib Nazir Mir

Published:
24th Jan 2020

Abstract:
INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning (ML) has helped in solving problems of computer vision, natural la…INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of
the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning
(ML) has helped in solving problems of computer vision, natural language processing, speech recognition etc.

OBJECTIVES: This paper aims to analyse the effect of feature type selection i.e. MFCC and TQWT on the efficiency of
voice based PD detection system along with the use an ensemble learning based classifier for this task.

METHODS: Hence, in this work, various machine learning models, including Logistic Regression, Naive Bayes, KNN,
Random Forest, Decision Tree, SVM, MLP, and XGBoost, have been employed and explored for PD detection purpose.
The task of Feature selection was also done using minimum-Redundancy and Maximum-Relevance (mRMR) and
Recursive Feature Elimination (RFE) techniques.

RESULTS: The results of the XGBoost with mRMR feature selection, outperformed all other models with a high accuracy
of 95.39% and precision, recall and F1-score of 0.95 each, when both MFCC and TQWT features were selected.

CONCLUSION: The results obtained strongly support the use of XGBoost model for the voice sample based detection of
PD along with mRMR feature selection technique.
more »
IoT Based Hybrid System for Patient Monitoring and Medication
Appears in:
phat 18: e1

Authors:
Naveen Kumar, Surya Narayan Panda, Preethi Pradhan, Rajesh Kumar Kaushal

Published:
24th Jan 2020

Abstract:
BACKGROUND: In recent years, a lot of research work has been done to transmit vital parameters of the patient using IOT (Internet of Things). The information related to Patient is sent by means of WBAN (Wireless Body Area Network) and sensors to the healthcare database. This has been shown a grea…BACKGROUND: In recent years, a lot of research work has been done to transmit vital parameters of the patient using
IOT (Internet of Things). The information related to Patient is sent by means of WBAN (Wireless Body Area Network)
and sensors to the healthcare database. This has been shown a great potential to transmit vital parameter of the patient at a
remote location. Remote monitoring of patient(s), including vital signs, sound and video is to be transmitted, particularly
when a patient is in transit. By transmitting the imperative parameters of the patient to a specialist diminishes, the ideal
opportunity for starting treatment and permits the emergency crew to be better prepared. This will facilitate a doctor to
make a diagnostic conclusion.

OBJECTIVES: This study aims to develop an advance system to transmit the vital parameters of the patient to the smart
phone of the doctor at a remote location. After seeing essential parameters of the patient, the specialist can convey/endorse
the medication from the remote area.

METHODS: To monitor patient’s data in real-time an android application is developed which helps the medical staff to
monitor the patient from remote location. All the patient related information is stored in Real Time Clinical Database.

RESULTS: The proposed model will allow the doctors to monitor physiological parameters (HR, NIBP, Temp, ECG,
SPO2) of the patient, as well as endorse medication from the remote location. This system will be helpful to provide
virtual presence of the doctor inside the ambulance and to provide lifesaving drugs to the patient while the patient is in
transit.

CONCLUSION: The present invention provides a fully integrated system for the doctor to view the vital signs of the
patient from the remote location in real-time.
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:
19th Nov 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 »
Indoor Localization Services for Hearing Aids using Bluetooth Low Energy
Appears in:
phat 19(18): e5

Authors:
Michał Kapiczynski, Sergi Rotger-Griful, Stefan Wagner

Published:
15th May 2019

Abstract:
BACKGROUND: Hearing loss is a common disorder which is usually treated with hearing aids. The context of use, whether being outside, inside of large or small rooms, in a quiet or noisy environment, or talking with someone near or at a distance, often requires the hearing aid user to adjust the sett…BACKGROUND: Hearing loss is a common disorder which is usually treated with hearing aids. The context of use, whether being outside, inside of large or small rooms, in a quiet or noisy environment, or talking with someone near or at a distance, often requires the hearing aid user to adjust the settings of the hearing aids manually in order to reach an optimal or even acceptable hearing experience. Knowing the context of a hearing aid user in the home setting, including the position within the home, could likely automate and optimize relevant hearing aid settings as needed. Several modern hearing aids are already equipped with Bluetooth Low Energy (BLE) antennas enabling their connectivity with smartphones and other compatible devices. This allows for the development of positioning and localization services using BLE.

OBJECTIVES: The aim of this study was to investigate the potential of using the BLE radio signal of hearing
aids to provide indoor localization and positioning support when used in combination with fixed radio access points in a home setting.

METHODS: A research platform based on a single Oticon hearing aid and three embedded Raspberry Pi
computers placed at three strategic locations in a home setting was developed. The Raspberry Pis used the two statistical learning methods K-Nearest Neighbors and Decision Trees for non-obtrusive detection, classification, and determination of the location of the hearing aid. The efficacy of the research platform was evaluated during two hearing experience relevant efficacy studies: 1) room classification and 2) proximity detection of hearing aid users to their conversation
partners. RESULTS: Room classification results provided an accuracy of 88.79% and it was found feasible and reliable to differentiate whether the hearing aid user was within a comfortable conversation distance or not.

CONCLUSION: These
results open up for a wide range of audiological applications in indoor environments for supporting new context-aware services for improving the hearing experience of the users.
more »
Privacy Preserving Fingerprint Authentication at the Cloud Server for eHealth Services
Appears in:
phat 19(18): e4

Authors:
S Rajarajan, R Venkata Kausik, M Sree Charan, PLK. Priyadarsini

Published:
15th May 2019

Abstract:
BACKGROUND: Restricting entry and access to highly sensitive systems like e-Health services is vital to protect the privacy and confidentiality of customers. User authentication is implemented predominantly done using Bio-metric schemes. But there is a challenge in securely storing the fingerprint …BACKGROUND: Restricting entry and access to highly sensitive systems like e-Health services is vital to protect the privacy and confidentiality of customers. User authentication is implemented predominantly done using Bio-metric schemes. But there is a challenge in securely storing the fingerprint templates. Since fingerprints are permanent, fingerprints getting
stolen will be a serious issue.

OBJECTIVE: The objective of this paper is to design a scheme to carry out the fingerprint match at the cloud server that will not compromise the fingerprints.

METHODS: When users input their fingerprint, a unique key is generated and it is sent to the server for verification.

RESULTS: We evaluated our proposed scheme and ascertained that it generates unique Fingerprint Identification Number for different fingerprints and the false rejection rate of our proposed scheme is within acceptable limit.

CONCLUSION: The proposed scheme overcomes the security threats to the stored fingerprints of users.
more »
A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm
Appears in:
phat 19(18): e3

Authors:
R. Devika, S. Sinduja, V. Subramaniyaswamy

Published:
15th May 2019

Abstract:
INTRODUCTION: Twitter has played an important role in the social life of people. The health-related tweets are extracted and find the spread of epidemic disease on network. It can provide as a starting place of individual data to learn the physical condition of users. OBJECTIVES: Key objective i…INTRODUCTION: Twitter has played an important role in the social life of people. The health-related tweets are extracted and find the spread of epidemic disease on network. It can provide as a starting place of individual data to learn the physical condition of users.

OBJECTIVES: Key objective is to develop graph-based algorithm to detect public health in online social network.

METHODS: The proposed method collect the tweets relating to general health in twitter using the min-cut algorithm. The algorithm finds the minimum cut off an undirected edge-weighted graph. The runtime of the algorithm seems to be faster than other graph algorithms. Min-cut is reliable and good in network optimization and prevents redundancy.

RESULTS: To evaluate the performance, we utilize the health dataset on the detection of epidemic disease. The proposed method using a graph-based algorithm is the best in terms of accuracy, precision, and recall. With respect to the confusion matrix, Min-cut provides the highest true positive when compared to Text rank and K-Means algorithm.

CONCLUSION: Proposed health detection method using graph-based algorithm is better than Text Rank and K-Means in all aspects.
more »
An Effective and Reliable Computer Automated Technique for Bone Fracture Detection
Appears in:
phat 19(15): e2

Authors:
CMAK Zeelan Basha, T. Maruthi Padmaja, G. N. Balaji

Published:
15th May 2019

Abstract:
INTRODUCTION: In the year 1895 the X-ray images were discovered. Since then the medical imaging has got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the evolution of Computer aided Diagnosis(CAD). OBJECTIVES: To develop a Computer Aided Diagnosis…INTRODUCTION: In the year 1895 the X-ray images were discovered. Since then the medical imaging has
got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the
evolution of Computer aided Diagnosis(CAD).

OBJECTIVES: To develop a Computer Aided Diagnosis (CAD) system to detect the bone fracture which
helps the radiologists (or) the Orthopaedics by interpreting the medical images in short duration.

METHODS: In this paper, an effective automated bone fracture detection is proposed using enhanced Haar
Wavelet Transform, Scale-Invariant Feature Transform (SIFT) and back propagation neural network. The
former two techniques are used for feature extraction and the latter one is used for classification of fracture
images. Simultaneously, the usage of enhanced Haar Wavelet Transforms and SIFT are phenomenally improves the quality of the X-ray image. Further in this work, k-means clustering based ‘Bag of Words’ methods are used to extract enhanced features extracted from SIFT. The classification phase of this proposed technique uses the classical back propagation neural network that contains 1024 neurons in 3-layers.

RESULTS: The experimental validation of this proposed scheme performed using nearly 300 different
bone fractures x-ray images confirmed a better classification rate of 93.4%.

CONCLUSIONS: The experimental results of the proposed computer aided technique are proven to be
better than the detection technique facilitated with the traditional SIFT technique.
more »
Hybrid Robust Image Steganography approach for the secure transmission of biomedical images in Cloud
Appears in:
phat 19(18): e1

Authors:
Arunkumar S, Subramaniyaswamy V, Logesh R

Published:
15th May 2019

Abstract:
INTRODUCTION: A rich patient may reside in their home and may prefer to take treatment with all the medical appliances inside the premise. A patient may take treatment in the less specialized hospital, which may be taking a suggestion from a specialist working in a specialized center. In such a sce…INTRODUCTION: A rich patient may reside in their home and may prefer to take treatment with all the medical appliances inside the premise. A patient may take treatment in the less specialized hospital, which may be taking a suggestion from a specialist working in a specialized center. In such a scenario, bio-medical images need to be transmitted over the public network. Bio-medical images sent in plain form suffer from confidentiality and integrity problem.

OBJECTIVES: To overcome the above problem, medical records are always hidden in the cover image so that others do not know what is being sent.

METHODS: In our proposed scheme, the chosen cover image gets divided into the following three planes, namely: R plane, G plane, and B plane. The co-occurrence matrix is computed for each plane by dividing it into 16 x 16 pixels block and then the embedding map is generated from it. The biomedical image is also divided into 8 x 8 pixels block and
is hidden into the chosen cover image block using the embedding map. Both cover image blocks and secret image blocks are transformed by RIWT. R matrix of QR decomposition of the cover image and secret image blocks are used in embedding At the end of the embedding phase. These three planes are merged into an RGB image to produce a stego image.

RESULTS: To assess the performance of our scheme, parameters like imperceptibility, robustness, and security are considered. With respect to imperceptibility, PSNR values of the stego images are over 50. With respect to robustness, average NCC values between the original secret and the attacked secret is 0.94. With respect to security, stego image cannot be detected easily if it has any secret.

CONCLUSION: From the experimental results, our scheme is proved to be better with respect to these three selected parameters.
more »
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 »
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 »

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

Authors:
Manik Sharma, Natalia Romero

Downloads:
1344

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:
1308

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:
1244

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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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:
1208

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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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:
857

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:
855

Abstract:
The aim of this paper is to provide a consistent development path enabling the re-usability of in house legacy systems or architectures towards their re-design, in order to ensure compliance with evolving standards, by using the new features of SysML for modelling variants. Modern standards evolve …The aim of this paper is to provide a consistent development path enabling the re-usability of in house legacy systems or architectures towards their re-design, in order to ensure compliance with evolving standards, by using the new features of SysML for modelling variants. Modern standards evolve quickly, include advanced functionalities and operations and support diverse implementations. System industries need to cope with such standards changes by modifying their current technologies. This paper shows how a novel engineering process (SysML modelling) could be employed to define consistently the specification and the migration procedure of legacy systems to their variants. Within this work SysML characteristics such as package and block diagrams, are employed, with an emphasis on variability modelling, as a basis for standard compliant architecture implementation, thus providing design flexibility and reusability at several abstraction levels. As an illustration of our proposed method we present models of two variant Physical Layer structures for IEEE-802.15.6 Standard for e-Health Applications. The advanced SysML features are used to target the re-usability of a legacy Narrow-Band (NB) physical layer subsystem for the Wireless Body Area Network standard and to implement the alternative Ultra-Wide Band (UWB). Therefore, we contend that such methods bring potential benefits to those needing to ensure compliance when producing product variants.
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Reducing alarm fatigue: exploring decision structures, risks, and design
Appears in:
phat 17(10): e2

Authors:
Mustafa Hussain, James Dewey, Nadir Weibel

Downloads:
788

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

Authors:
Anil Chacko, Thaier Hayajneh

Downloads:
785

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

Authors:
Dong Qin, Ming-Chun Huang

Downloads:
719

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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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:
716

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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Publisher: EAI
ISSN: 2411-7145
Number of Volumes: 5

Last Published: 15th May 2019

EAI Endorsed Transactions on Pervasive Health and Technology

Natalia Romero1

Manik Sharma2

Natalia Romero¹

Manik Sharma²