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Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/phat/index

Editor(s)-in-Chief: Nenad Filipovic, Jun Hu, Emeritus Editors-in-Chief and Gonçalo Marques

EAI Endorsed Transactions on Pervasive Health and Technology is open access, a peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining, knowledge management, IT applications to the 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. From 2023, the journal follows a continuous publication model.

INDEXING: Scopus (CiteScore: 3.3), Compendex, DOAJ, ProQuest, EBSCO, DBLP

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

.

  • Editors-in-Chief
  • Nenad Filipovic, University of Kragujevac, Serbia
  • Jun Hu, Eindhoven University of Technology, The Netherlands
  • Area Editors
  • Alberto Antonietti (École Polytechnique Fédérale de Lausanne, Switzerland)
  • Alejandro Dominguez Rodriguez (Autonomous University of Baja California)
  • Alessandro Ruggiero (University of Salerno, Italy)
  • Alexandros Tzallas (Technological Educational Institute of Epirus)
  • Andjela Blagojevic (University of Kragujevac, Serbia)
  • Anind Dey (University of Washington)
  • Ansar-Ul-Haque Yasar (Hasselt University, Belgium)
  • Antonella Carbonaro (Universita' di Bologna, Italy)
  • Arijit Ukil (Tata Consultancy Services)
  • Asimina Kiourti (The Ohio State University)
  • AtaUllah Ghafoor (National University of Modern Languages, Islamabad, Pakistan.)
  • Bert Arnrich (Boğaziçi University, Istanbul, Turkey)
  • Boban Stojanovic (University of Kragujevac, Serbia)
  • Branko Arsic (University of Kragujevac, Serbia)
  • Constantinos Pattichis (University of Cyprus, Cyprus)
  • Daojing He (South China University of Technology, China)
  • Djordje Jakovljevic (Coventry University, UK)
  • Eduard Babulak (National Science Foundation, USA)
  • Emilija Stojmenova (Duh University of Ljubljana)
  • Emilio Serrano (Technical University of Madrid, Spain)
  • Frank Wallhoff (Jade University of Applied Sciences)
  • Giorgos Giannakakis (Foundation for Research and Technology Hellas, Greece)
  • Honggang Wang (UMASS. USA)
  • Kashif Saleem (King Saud University)
  • Laszlo Bokor (BME, Hungary)
  • Lazar Dasic (University of Kragujevac, Serbia)
  • Marcela Deyanira Rodriguez Urrea (Autonomous University of Baja California)
  • Marko Robnik-Sikonja (University of Ljubljana, Slovenia)
  • Mauro Femminella (University of Perugia, Italy)
  • Melina Frenken (Jade University of Applied Sciences)
  • Michalis Zervakis (Technical University of Crete, Greece)
  • Milos Ivanovic (University of Kragujevac, Serbia)
  • Milos Kotlar (University of Belgrade, Serbia)
  • Mohammad Upal Mahfuz (University of Wisconsin-Green Bay)
  • Mojtaba Taherisadr (University of Michigan)
  • Mosabber Uddin Ahmed (University of Dhaka)
  • Nadeem Javaid (COMSATS Institute of Information Technology, Islamabad, Pakistan)
  • Netzahualcoyotl Hernandez-Cruz (Oxford University, UK)
  • Nikolaos Bourbakis, Wright State University in Ohio, United States
  • Pan Zheng, University of Canterbury, New Zealand
  • Pietro Cipresso (Istituto Auxologico, Milan, Italy)
  • Razan Hamed (New York Institute of Technology, NY, USA)
  • Riccardo Martoglia (University of Modena and Reggio Emilia, Italy)
  • Saikishor Jangiti (SASTRA Deemed University, India)
  • Silvia Serino (Catholic University of Milan, Italy)
  • Stefan Rahr Wagner (Aarhus University)
  • Tanvir Zia (School of Computing & Mathematics, Charles Sturt University, Australia)
  • Tessa Dekkers (Delft University, The Netherlands)
  • Tijana Sustersic (Faculty of Engineering, University of Kragujevac)
  • Veljko Milutinovic (University of Belgrade, Serbia)
  • Venet Osmani (CREATE-NET, Trento, Italy)
  • Wilko Heuten (OFFIS, Germany)
  • Zhihan Lv (Qingdao University, China)
  • Zoran Bosnic (University of Ljubljana, Slovenia)
  • Ognjen Pavic (Institute for Information Technology, University of Kragujevac, Serbia)
  • Analúcia Schiaffino Morales (Federal University of Santa Catarina, Brazil)
  • Emeritus Editors-in-Chief
  • Gonçalo Marques, Polytechnic of Coimbra, Portugal

  • Exploring Diverse Features: A Thorough Survey for Anxiety Disorders

    Appears in:
    phat 25(1):

    Authors:
    Saravjeet Singh , Aditi Moudgil , Rakhi Nagpal

    Published:
    6th Jun 2025

    Abstract:
    INTRODUCTION: The prevalence of mental health issues, mainly anxiety disorders, has risen significantly in today’s fast paced world. Thus, giving the imperative to confront the challenges associated in identifying these issues. OBJECTIVE: The objective of this review paper is to signify the importance of different features or parameters (acoustic, prosodic, linguistic, facial, neuroimaging, psychological, and physiological) being extracted from different data modalities (audio, video, psychological, physiological, textual, and neuroimaging) that are being used to assess different kinds of mental disorders. METHODS: Considering the systematic literature review technique, a total of 102 studies have been identified in the field of anxiety disorders and mental health issues spanning the years 2015 to 2024. By considering diverse features being used to diagnose different kinds of anxiety disorders, this paper provides a foundation for future research that will help researchers to design the new strategies and techniques to handle the anxiety disorder. CONCLUSION:  This comprehensive review paper outlines the details of diverse features extracted across various data modalities, contributing significantly to the prediction of a wide range of anxiety disorders.

  • Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens

    Appears in:
    phat 25(1):

    Authors:
    Sylvain Moreno, Lucy Yardley, Anthony Duffy, Sarah E Bennett

    Published:
    2nd Jun 2025

    Abstract:
    INTRODUCTION: Co-design has become a fundamental pillar of formative digital health research. Typically, this approach involves in–person workshops that involve a rich but limited amount of data. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. This is a novel, exploratory approach in mHealth design that may appease common health research concerns surrounding reliability and validity, whilst providing swifter feedback to meet product development timelines. OBJECTIVES: The objective of this exploratory single case study was to explore the virtual, crowdsourced, co-design of Sleep Solved, an educational mHealth sleep app designed with teens. In doing so, we wished to learn which virtual methods were used to engage teens in the co-design and to explore how these virtual co-design methods can be adapted for large-scale ideation and testing. METHODS: We conducted an enquiry-based iterative case study utilising the Bayazit 3-stage model. 85 teens participated over 11 months. Data was thematically analysed over several design iterations. RESULTS: Rapid virtual feedback allowed for quick pivots in a short time frame. Four stages of feedback from teens led to iterative changes to scientific information contextualisation and user experience, from lo-fidelity mock-ups through to a coded app beta. CONCLUSION: The co-design of Sleep Solved exemplified the potential of virtually crowdsourcing teens in mHealth. Key to this evolution will be the ability to leverage big data utilising AI and machine learning approaches to data collation and synthesization, such that meaningful and contextual findings can be applied in line with software development timelines.

  • Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders

    Appears in:
    phat 25(1):

    Authors:
    Georgia Karanasiou, Manolis Tsiknakis, Ioannis Kyprakis, Vasileios Skaramagkas, Dimitrios Fotiadis

    Published:
    2nd Jun 2025

    Abstract:
    INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition that can develop after exposure to traumatic events, often resulting in symptoms that severely impair daily functioning. Current diagnostic methods largely rely on subjective assessments, highlighting the need for objective, non-invasive tools to improve diagnostic precision.  OBJECTIVES: This study aims to develop and validate an innovative deep learning approach using autoencoder neural networks to detect PTSD through analysis of electrocardiography (ECG) signals. The goal is to provide a reliable and sophisticated diagnostic method that bridges computational and clinical domains.  METHODS: We employed autoencoder neural networks to analyze ECG data collected from wearable heart zone sensors. This unsupervised learning model was trained to detect subtle anomalies in the ECG signals that may serve as biomarkers for PTSD. The methodology was evaluated using data collected from individuals with and without PTSD symptoms.  RESULTS: The proposed model demonstrated strong potential as an objective diagnostic tool, successfully identifying patterns in ECG signals associated with PTSD. The analysis confirmed the model’s ability to distinguish PTSD-related anomalies with 83% accuracy.  CONCLUSION: This research introduces a novel, non-invasive diagnostic methodology for PTSD using deep learning and wearable ECG data. The findings support the model's value as a potential objective biomarker, contributing to more precise psychiatric diagnostics and expanding the role of machine learning in healthcare.

  • Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning

    Appears in:
    phat 25(1):

    Authors:
    Saeid Pourroostaei, Liyun Gong, Miao Yu

    Published:
    17th Apr 2025

    Abstract:
    Human Gait Analysis is crucial in healthcare applications, with numerous research works focusing on machine learning and deep learning approaches for tasks such as abnormal gait detection and gait quality assessment. However, developing such models requires collecting and sharing a significant amount of patient data, raising privacy concerns. In this study, we introduce the world’s first technique for constructing a deep neural network model to stratify patients’ pain levels based on video recordings of timed up-and-go activities, while ensuring privacy preservation through modern federated learning algorithms. Our experimental results demonstrate the effectiveness of this technique in accurately stratifying LBP levels without the need for data sharing among local clients to maintain privacy.

  • Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning

    Appears in:
    phat 25(1):

    Authors:
    Chan Chi Leung, Liyun Gong, Sheldon Mccall, Ifeoma Ochi, Miao Yu, Pandey Shourya Prasad, Afreen Naz

    Published:
    16th Apr 2025

    Abstract:
    Gait analysis plays a pivotal role in diagnosing a spectrum of neurological and musculoskeletal disorders. Variations in gait patterns often serve as early indicators of underlying health conditions, underscoring the importance of precise and timely analysis for effective intervention and treatment. In recent years, computer vision techniques have emerged as robust tools for automated gait analysis, offering non-invasive, costeffective, and scalable solutions. However, existing approaches often overlook the critical aspect of privacy preservation. In this study, we propose the world’s pioneering computer vision-based abnormal gait detection system with a privacy-preserving mechanism. Specifically, we extract 2D skeletons from encrypted images using a deep neural network model, which is facilitated by an optical system incorporating a custom-made refractive optical element. These extracted features are then fed into machine learning models for the detection of normal versus abnormal gait patterns. Evaluations across various models including random forest, decision tree, K-nearest neighbor, support vector machine, neural network, and convolutional neural network reveal that the random forest model attains the highest classification performance based on 2D skeletons extracted from encrypted images.

  • Sport injury imaging for deep blood flow distribution with laser speckle

    Appears in:
    phat 25(1):

    Authors:
    Sravan Kumar Reddy M., Fu Huang, Dezhi Geng

    Published:
    11th Apr 2025

    Abstract:
    When laser speckle program technology is used to measure the blood flow distribution of deep tissues (such as subcutaneous tissue) in sports injuries, the deep blood flow characteristics of sports injuries contain a large amount of turbid tissue fluid. Laser passing through turbid tissue fluid will produce strong interference static speckle, masking the dynamic speckle of blood flow distribution, resulting in poor imaging effect of blood flow characteristics. Propose laser speckle imaging optimization technology and apply it to the measurement of deep tissue blood flow distribution in sports injuries. Based on the principle of laser speckle imaging technology, the problems in laser speckle imaging of deep blood flow distribution characteristics in sports injuries are analyzed. An exponential Laplace loss function is introduced to reduce the amplitude of changes in blood flow characteristics in intra class sports injuries and collect deep blood flow distribution characteristics in sports injuries; On the basis of calculating the laser speckle contrast ratio, the blood volume flow rate is determined, and the blood volume flow rate data is combined with the laser speckle contrast ratio to achieve imaging of deep blood flow distribution in sports injuries. The experimental results show that the improved laser speckle imaging technology has better imaging effects in imaging the deep blood flow distribution of sports injuries; Compared with the comparison method, the DICE coefficient, average accuracy MPA, and global imaging index have all improved, indicating that this method can effectively improve the imaging effect and is feasible.

  • Battery signal control model for large-scale IoT medical monitors under multipath interference

    Appears in:
    phat 25(1):

    Authors:
    Shengnan Zhang, Shuhua Yang, Ding Chen, Syed Atif Moqurrab

    Published:
    11th Apr 2025

    Abstract:
    INTRODUCTION: In large-scale IOT medical monitors, the accurate control of battery signals has been facing the problem of multipath interference. Multipath interference causes the receiver to receive multiple signals propagating through different paths and interfering with each other, which results in an imbalance in the battery signal control based on the time delay of the "transmit-receive" signals. OBJECTIVES: To solve the multipath interference problem of existing battery signal control, this paper designs a battery signal control model for a large-scale IoT medical monitor. METHODS:Firstly, this paper uses time synchronization to align the time between the receiver and the transmitter to synchronize the communication signals of the acquisition system; Next, a transverse time-domain filter is used for modulation filtering; Then, a judgment feedback equalization algorithm is introduced in combination with a full-feedback filter to suppress the inter-code interference and improve the signal quality; Finally, a fractional interval equalizer is designed to adjust the weight coefficients of the equalizer taps, and implement intelligent battery signal control in multi-hop communication under multipath interference based on fractional interval and bit error rate (BER) feedback modulation. RESULTS: Experimental results have shown that after using the method described in this paper to control the communication signal of the monitor battery, the output signal is relatively stable, and the BER reaches 1×10-4 when the signal-to-noise ratio is equal to 18dB. The BER is low, and the carrier-to-noise ratio of the output signal is 0.73~0.85. The carrier-to-noise ratio always remains above 0.73. CONCLUSION:The technologies effectively deal with complex network environment and channel condition variation, ensuring balanced system control, and the signal control effect is outstanding.

  • Evaluating Physicians’ Satisfaction with Using eHealth System for Managing Patients in Primary and Secondary Care

    Appears in:
    phat 25(1):

    Authors:
    Irena Stojmenovska, Viktor Denkovski, Vladimir Radevski, Goce Gavrilov

    Published:
    11th Apr 2025

    Abstract:
    INTRODUCTION: eHealth systems are raising both patient satisfaction and medical care. The proper workflow regulations and data exchange between primary and secondary healthcare are crucial. OBJECTIVES: Investigation of the major determinants influencing the physicians’ satisfaction while using an eHealth information system. METHODS: A survey of primary and secondary healthcare medical professionals was conducted in R.N. Macedonia. The categorical variables from the data analysis were presented and a logistic regression was carried out. RESULTS: The multiple logistic regression model was statistically significant for sufficient evidence to reject the null hypothesis in which the overall satisfaction rating of the eHealth system usage for managing patients and other healthcare services will not be affected by the other variables in favour of the alternative Ha. CONCLUSION: Various factors between primary and secondary healthcare professionals regarding system’s usage satisfaction are presented and studied. Various issues were revealed between both parties that should serve the policymakers and medical authorities for further improvements.

  • SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection

    Appears in:
    phat 25(1):

    Authors:
    Stefanos Kollias, James Wingate, Dimitrios Kollias, Anastasios Arsenos

    Published:
    2nd Apr 2025

    Abstract:
    This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection. Our approach includes a combination of vision-language models that segment the CT scans, which are then fed to a deep neural architecture, named RACNet, for Covid-19 detection. In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases. At first, SAM produces multiple part-based segmentation masks for each slice in the CT scan; then CLIP selects only the masks that are associated with the regions of interest (ROIs), i.e., the right and left lungs; finally SAM is given these ROIs as prompts and generates the final segmentation mask for the lungs. Experiments are presented across two Covid-19 annotated databases which illustrate the improved performance obtained when our method has been used for segmentation of the CT scans.

  • The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models

    Appears in:
    phat 25(1):

    Authors:
    Gautam Siddharth Kashyap, Shina Samuel Kolawole, Miao Yu, Olamide Emmanuel Kolawole

    Published:
    2nd Apr 2025

    Abstract:
    The research paper aims to assess ML models for video-recorded gaits with an aim of classifying people into high or low risks to fall groups. Several ML algorithms were tried employing OpenPose for CV, with RF showing the best outcomes: 93% accuracy along with F1-score as well as balanced sensitivity (93.50%) as well as specificity (92.50%). Some important determining factors were speed per unit distance, angle among other statistical measures. In comparison to wearables-based DL approaches plus traditional fall detection methods, this study’s approach showed higher accuracy and adaptability within health care settings.

  • Skin Disease Classification Using CNN Algorithms

    Appears in:
    phat 23(1):

    Authors:
    None

    Downloads:
    1817

    Abstract:
      INTRODUCTION: Dermatological disorders, particularly human skin diseases, have become more common in recent decades. Environmental factors, socioeconomic problems, a lack of a balanced diet, and other variables have all contributed to an increase in skin diseases in recent years. Skin diseases can cause psychological suffering in addition to physical injury, especially in people with scarred or disfigured faces. OBJECTIVES: The use of artificial intelligence or computer-based technologies in the detection of face skin disorders has advanced dramatically over time. Even for highly experienced doctors and dermatologists, identifying skin disorders can be tricky since many skin diseases have a visual affinity with the surrounding skin and lesions. METHODS: Today, the majority of skincare specialists rely on time-consuming, traditional methods to identify disorders. Even though several research have demonstrated promising results on the picture classification job, few studies compare well-known deep learning models with various metrics for categorizing human skin disorders. RESULTS: This study examines and contrasts various skin illnesses in terms of cosmetics and common skin concerns. Our dataset includes over 25000 of the eight most common skin disorders. Convolutional neural networks have shown imaging performance that is comparable to or greater than that of humans. We used 11 different network algorithms to identify the illnesses in the sample and compared the results. CONCLUSION: To adjust the format of incoming photographs, we do certain image pre-processing and image scaling for each model. ResNet152 beat other deep learning methods in terms of recall, accuracy, and precision on a test dataset of 1930 images.

  • The associations between mental health and environmental factors in New Zealand: A region-based analytical study

    Appears in:
    phat 22(31): 1

    Authors:
    Morten Viehoff, Leona Huang, Daniel Grossman, Jianwei Jiang, Pan Zheng

    Downloads:
    909

    Abstract:
    INTRODUCTION: Connections between environmental factors and mental health issues have been postulated in many different countries around the world. Previously undertaken research has shown many possible connections between these fields, especially in relation to air quality and extreme weather events. However, research on this subject is lacking in New Zealand, which is difficult to analyse as an overall nation due to its many micro-climates and regional differences. OBJECTIVES: The aim of this study and subsequent analysis is to explore the associations between environmental factors and poor mental health outcomes in New Zealand by region and predict the number of people with mental health-related illnesses corresponding to the environmental influence. METHODS: Data are collected from various public-available sources, e.g., Stats NZ and Coronial services of New Zealand, which comprised four environmental factors of our interest and two mental health indicators data ranging from 2016 up until 2020. The four environmental factors are air pollution, earthquakes, rainfall and temperature. Two mental health indicators include the number of people seen by District Health Boards (DHBs) for mental health reasons and the statistics on suicide deaths. The initial analysis is carried out on which regions were most affected by the chosen environmental factors. Further analysis using Auto-Regressive Integrated Moving Average(ARIMA) creates a model based on time series of environmental data to generate estimation for the next two years and mental health projected from the ridge regression. RESULTS: In our initial analysis, the environmental data was graphed along with mental health outcomes in regional charts to identify possible associations. Different regions of New Zealand demonstrate quite different relationships between the environmental data and mental health outcomes. The result of later analysis predicts that the suicide rate and DHB mental health visits may increase in Wellington, drop-in Hawke's Bay and slightly increase in Canterbury for the year 2021 and 2022 with different environmental factors considered. CONCLUSION: It is evident that the relationship between environmental and mental health factors is regional and not national due to the many micro-climates that exist around the nation. However, it was observed that not all factors displayed a good relationship between the regions. We conclude that our hypotheses were partially correct, in that increased air pollution was found to correlate to increased mental health-related DHB visits. Rainfall was also highly correlated to some mental health outcomes. Higher levels of rainfall reduced DHB visits and suicide rates in some areas of the country.

  • Analyzing How AI And Emotional Intelligence Affect Indian IT Professional’s Decision-Making

    Appears in:
    phat 23(1):

    Authors:
    Anita Shukla, Bhawna Singh, Alka Algnihotri

    Downloads:
    895

    Abstract:
    Artificial intelligence (AI) is transforming how we work and make choices, but it also poses ethical and societal issues including algorithmic discrimination and dehumanization. It is critical to take into account corporate culture, emotional intelligence, cooperation, communication, and constant learning when using AI systems in the workplace. It has been demonstrated that emotional intelligence increases AI adoption, efficacy, and performance across a variety of sectors. But ethical concerns and trouble making decisions are also important. Effective collaboration, communication, and corporate culture are crucial for successful AI adoption, and continuing learning and development are essential for enhancing decision-making abilities. AI ethics in the workplace necessitate a comprehensive strategy that considers both technical and non-technical aspects. This study looks at the benefits of emotional intelligence, moral concerns, effective stakeholder and IT specialist engagement, organisational culture, and potential threats of artificial intelligence (AI) in decision-making. The study underlines the value of continuous AI learning and development.

  • Human Computer Interaction Applications in Healthcare: An Integrative Review

    Appears in:
    phat 23(1):

    Authors:
    Ritunsa Mishra, Rabinarayan Satpathy, Bibudhendu Pati

    Downloads:
    778

    Abstract:
    INTRODUCTION: Human computer interaction (HCI) interprets the design model and the uses of computer technology which focuses on the interface between the user and the computer. HCI is a very important factor in the design of software-oriented decision-making ideas in health-care organizations and also it assists in accurate detection of image, disease including safety of the patients. OBJECTIVES: There are some pitfalls arises over some previous works on cloud based HCI applications. For that reason, to masafety, patient’s safety we wanted to work on explainable artificial intelligence (x-AI) and human intelligence in conjunction with HCI in various fields and algorithms to pro-vide transparency to the user. This may also use some web-based technologies and digital platforms with HCI for development of quality, safety and usability of the patients. METHODS: The purpose of this study about the communication between the HCI design and healthcare system through client and apply that method to the information system of Healthcare department to analyse the functions, effects and outcomes. RESULTS: The integration of explainable artificial intelligence (x-AI) and human intelligence with Human-Computer Interaction (HCI) demonstrated promising potential in enhancing patient safety and optimizing healthcare processes.     CONCLUSION: By leveraging web-based technologies and digital platforms, this study established a framework for improving the quality, safety, and usability of healthcare services through effective communication between HCI design and healthcare systems.

  • Masked GANs for Face Completion: A Novel Deep Learning Approach

    Appears in:
    phat 24(1):

    Authors:
    D Khasim, Anshuman Sharma, Tejaswini Kar, Biswaroop Nath

    Downloads:
    769

    Abstract:
    INTRODUCTION: Recent deep learning based image editing methods have achieved promising results for removing object in an image but fail to generate appreciable performance for removing large objects of complex nature, especially mask from facial images. Towards this goal the objective of this work is to remove mask objects in facial images. In this study, authors propose a novel approach for face completion using Generative Adversarial Networks (GANs) that utilize masked data. This technology can help in image restoration and preservation, thus enabling us to cherish those memories that are held dear to our hearts. OBJECTIVES: Train a GAN to learn the mapping from incomplete to complete face images by utilizing a masked input image. METHODS: The discriminator is trained to distinguish between face images and full ground truth images. Our results indicate that our technique generates high-quality, realistic facial images that are visually comparable to the ground truth and that it can generalise to new faces that were not encountered during training. RESULTS: Our findings indicate that GANs with masked inputs are a good approach for generating whole face images from partial or masked data. CONCLUSION: Our experimental findings show that our method produces facial images of great quality and realism that are visually equivalent to the actual thing. Our proposed approach can also be applied to fresh faces that weren’t seen. The performance can still be improved further using larger dataset. Also, further investigation into adversial attacks may help in improving performance. This technology can be further utilized for developing realtime mask removal software as well.

  • Prediction of Anemia using various Ensemble Learning and Boosting Techniques

    Appears in:
    phat 23(1):

    Authors:
    Nalluri Shweta

    Downloads:
    711

    Abstract:
    INTRODUCTION: Anemia is a disease of great concern. It is mainly seen in people who are deficient in several vitamins like B12 and those who are deficient in iron. Neglecting the situation and leaving it untreated could lead to severe consequences in the future. Hence it is of great importance to predict Anemia in an individual and treat it in the optimum stage. OBJECTIVES: In this paper, machine learning was used for the prediction of Anemia. METHODS: The dataset used for this was formed by combining different datasets from Kaggle. The accuracy of various machine learning techniques was evaluated to find out the best one. Along with the supervised learning algorithms like Random Forest, SVM, Naive Bayes etc., Linear Discriminant Analysis, Quadratic Discriminant Analysis and ensemble learning methods were also performed. RESULTS: Upon evaluation, among the best performers, the execution time was also taken into consideration to determine which classifier works well. Among all the algorithms used, XGboost worked the best with an optimum execution time. CONCLUSION: The conclusion is that for the data used in the work, XGboost results as the best model.

  • A Systematic Review on the Adoption of Blockchain Technology in the Healthcare Industry

    Appears in:
    phat 23(1): e3

    Authors:
    Mazida Ahmad, Fathey Mohammed, Mahmood A. Bazel

    Downloads:
    671

    Abstract:
    INTRODUCTION: Blockchain technology is a distributed ledger, decentralized, and cryptographically secure technology which has garnered considerable interest in different sectors including healthcare. It can enable better trust, security, management, and transparency of healthcare data, processes, and transactions resulting improving quality of care. Despite the fact of the increasing number of research investigating the applications/potentials of blockchain in healthcare, there is a scarcity of comprehensive reviews that focuses on the factors that influence its adoption in the healthcare industry. OBJECTIVES: This review aims to summarise existing studies regarding the adoption of blockchain technology in the healthcare industry. This review presents a detailed review of existing empirical studies investigating the factors influencing blockchain adoption in healthcare by highlighting the research methodologies, targeted stakeholders, adoption theories/models used, and the influential factors explored in each of these studies. Careful syntheses of these studies would enable researchers and partitioners to acquire a wide knowledge and understand various opportunities and challenges of blockchain implementation in healthcare. METHODS: Inspired on  “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines, the study's scope and research questions are established, Scopus database is selected as an information resource, search strategy,  and inclusion and exclusion criteria for document selection is developed. This review was conducted in August 2022. From 223 articles found in the search,  12 met the eligibility criteria and were selected to be extensively analyzed in this review. RESULTS: This review reveals that very few empirical studies exist that sought to explore the significant factors influencing blockchain adoption in healthcare. The qualitative method was the most method employed, healthcare providers were the most targeted stakeholders, and most of the studies were not based on adoption theories/models. Privacy, government regulation, and trust were the most influential factors investigated in the studies. CONCLUSION: The utilization of blockchain can help handle many issues in healthcare systems and bring improved healthcare delivery. Little attention has been paid to highlight internal and external factors that would impact successful blockchain adoption in healthcare. Additionally, the evaluated research placed little attention on understanding how underlying factors interact, social structures and institutional mechanisms affect the adoption of blockchain in healthcare. The reasons why healthcare organizations are hesitant to implement blockchain are still not clear. There is a need to conduct more research to examine the factors influencing the decision of healthcare stakeholders to adopt blockchain by using adoption theories/models. The proposed framework of the factors in this study may contribute as a starting point for future blockchain adoption studies in the healthcare industry.

  • Evaluation of the effectiveness of personal electronic health assistants in monitoring patients with chronic diseases

    Appears in:
    phat 23(1):

    Authors:
    Manuel Benigno Villanueva De La Cruz, Roberto Carlos Dávila-Morán, Roxana Maribel Bustamante Vega, Lucio-Arnulfo Ferrer-Peñaranda, Cesar Lorenzo Torres Sime, Cecilia Isabel Lévano Zegarra, Julio Brayan Saldaña Narro

    Downloads:
    649

    Abstract:
    Introduction: Chronic diseases pose significant challenges in healthcare, which has driven the development of electronic health solutions. The effectiveness of these solutions in the management of diseases such as hypertension has generated interest, but further in-depth, evidence-based evaluation is required. Objective: The study aims to comprehensively evaluate how a customizable web platform, called "HyperVigilance", influences blood pressure control in hypertensive patients, considering additional variables such as patient satisfaction, quality of life and costs associated with treatment. In addition, the aim is to explore possible demographic factors that could moderate the results. Methodology: The study was conducted with a quasi-experimental research design that included an intervention group using the "HyperVigilance" platform and a control group receiving standard medical care. Statistical tests were applied and demographic factors such as age, gender and socioeconomic status were considered. Results: The use of the "HyperVigilance" platform resulted in a significant reduction in blood pressure, increased patient satisfaction and a marked improvement in quality of life, as well as a reduction in the costs associated with the treatment of hypertension. Conclusions: The study concludes that the "HyperVigilance" platform is effective in controlling blood pressure and improving quality of life in patients with hypertension. The results support the growing role of digital interventions in chronic disease management, but highlight the need for long-term studies and exploration of different populations for a more complete understanding of their impact.

  • E-appointments at primary care physicians during Covid-19 pandemic: viewpoint and satisfaction of medical consumers

    Appears in:
    phat 22(31): 2

    Authors:
    Goce Gavrilov, Viktor Denkovski

    Downloads:
    630

    Abstract:
    INTRODUCTION: Use of e-appointment systems in primary care (EASPC) is a common practice in western countries, however, there is no evidence of implementation in R. N. Macedonia and other similar countries in development. OBJECTIVES: This study explores the viewpoints and satisfaction of medical consumers (MC) on EASPC, and the impact of Covid-19 global pandemic concerning their appointments at primary care physicians. METHODS: A survey on MC above the age of 15 years was conducted in July 2020. The results were analysed in SPSS 23. RESULTS: The majority of participants were in favour on implementing an EASPC despite their neutral satisfaction with the walk-in method. Furthermore, they were confident in its effectiveness in the fight against spreading of Covid-19. CONCLUSION: The MC opinion and satisfaction is crucial when developing health care systems and their implementation as a mix of services. For the system to succeed, it must be financed and supported adequately and as such, further research is necessary to explore a real-time EASPC usage.

  • Remote medical video region tamper detection system based on Wireless Sensor Network

    Appears in:
    phat 22(31): 3

    Authors:
    Sujuan Li, Shichen Huang

    Downloads:
    622

    Abstract:
    INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper. OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video. METHODS: The sensor nodes in the sensing layer are responsible for collecting telemedicine video information and transmitting the information to the data layer. The data layer completes the storage of information and transmits it to the processing layer. The detection module of the processing layer detects the tampered area of the telemedicine video through two parts: suspicious moving point calculation and tamper detection, and transmits the detection results to the application display layer for display. RESULTS: The experimental results show that the designed detection system can accurately detect the tampered area in the telemedicine video, and the packet loss rate is significantly reduced, and the maximum packet loss rate is no more than 1%. CONCLUSION: The proposed detection system for remote medical video based on wireless sensor network can better meet the requirements of region tamper detection.
Publisher
EAI
ISSN
2411-7145
Number of Volumes
11
Last Published
2024-12-18
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