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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: Gonçalo Marques and Nenad Filipovic

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 2021, the journal publishes five issues per year. 

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
  • Gonçalo Marques, Polytechnic of Coimbra, Portugal
  • Nenad Filipovic, University of Kragujevac, Serbia
  • 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)

  • Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

    Appears in:
    phat 24(1):

    Authors:
    Zhiyong Li, Fangming Dai

    Published:
    4th Dec 2024

    Abstract:
    Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.

  • Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes

    Appears in:
    phat 24(1):

    Authors:
    Shuaishuai Zhang, Gang Chen

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: University physical education programs still largely use traditional methods without significant innovation in teaching or health evaluation. With the growing capabilities of Internet technology and artificial intelligence, there's a critical need to leverage these advancements to enhance the physical health assessments of college students. OBJECTIVES: The study proposes an integrated APP design for health data collection and analysis both inside and outside physical education classes, utilizing Internet technologies and intelligent learning algorithms. This is aimed at precisely analyzing and improving the health outcomes of university students by fostering more tailored and responsive physical education experiences.. METHODS: The method involves constructing an app design analysis index system, integrating a vulture search heuristic optimization algorithm with a convolutional neural network (CNN). This setup uses smart sports APP behavioral data as input to refine and optimize health data integration, aiming to enhance the analysis and feedback mechanisms within university sports programs. RESULTS: The implementation of this method showed that it meets real-time requirements while significantly improving the accuracy and efficiency of integrated APP design analyses for health data. The use of smart algorithms allows for more precise adjustments and feedback in physical education programs, suggesting a substantial improvement over traditional physical health monitoring and evaluation methods. CONCLUSION: The proposed APP design successfully integrates and analyzes health data, enhancing the management and evaluation of physical education programs. It represents a significant step forward in utilizing modern technology to address the stagnation in physical education health monitoring, with potential implications for broader educational and health management practices in universities. Future iterations of the APP could incorporate more diverse data inputs and advanced analytical features to further refine its effectiveness and usability.

  • Analysis of the use of electronic medical records and its effect on improving patient care

    Appears in:
    phat 24(1):

    Authors:
    Dimna Zoila Alfaro Quezada, Rosaria Luz Diaz Ramos, Lenka Angelita Kolevic Roca, Mirelly Velásquez Orellana, Carlos Víctor Mora Aguilar

    Published:
    4th Dec 2024

    Abstract:
    Introduction: One of the most important contemporary events related to the health field is the implementation of the electronic health record (EHR), which allows the logical and chronological consolidation of information concerning a patient. Objective: To analyze the influence of the implementation of electronic medical records on the improvement of patient care. Method: The study corresponds to a quantitative approach, non-experimental design, and causal correlational scope. The population and sample were 94 health and administrative workers of a health center in Metropolitan Lima. The questionnaire had 30 items, with response options using a Likert-type scale. Results: The use of electronic medical records was found to be 78.7% high, and patient care was found to be 70.2% efficient. It was found that the use of EHRs significantly influences patient care, given that p-value is 0.047<0.05. The dimensions of patient care, reliability, responsiveness, safety, empathy, and tangible aspects also obtained a p-value<0.05. Conclusions: It was found that most workers positively perceive the use of electronic medical records and that the existence of this system has beneficial effects on patient care, considering that the staff perceives high levels of reliability, responsiveness, safety, empathy, and tangible aspects.

  • Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks

    Appears in:
    phat 24(1):

    Authors:
    Aditi Sharan, Shikha Verma

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: Finding biomarkers that are closely associated with cancer-related traits is critical to the advancement of cancer research, especially when it comes to personalised treatment. The objective of this research is to explore multiple biomarker categories, including genetics, proteins, and chemicals, in order to better understand the complex terrain of cancer. OBJECTIVES: Few of the objectives include examining a variety of biomarker types, such as chemical, protein, and genetic markers and determining which important biomarker signatures correspond to each cancer hallmark. Also the study aims to perform a comparative analysis to show how the SVM model's features incorporating identified biomarkers improves classification performance. METHODS: The study includes NLP and ML techniques for the identification and classification of biomarkers for the hallmark of cancer dataset. RESULTS: The discovery of important biomarker signatures connected to every cancer hallmark is one of the study's primary findings. In addition, our new SVM-based classification model performed well in the multilabel text classification of PubMed abstracts, showing a significant improvement in performance when the biomarkers were used as features. CONCLUSION: To sum up, this study makes a substantial contribution to the area of cancer research by identifying important biomarker signatures connected to many cancer hallmarks.

  • Construction and effect evaluation of a hierarchical training system for nosocomial infection based on hospitals at all levels

    Appears in:
    phat 24(1):

    Authors:
    R Chang, D Meng

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: Nosocomial infection is a critical global public health issue. The education of medical personnel can effectively enhance compliance with nosocomial infection protocols and reduce the incidence of such infections. However, the current training provided to third-party staff is inadequate, necessitating an urgent enhancement of their knowledge on nosocomial infection through effective and tailored training programs. OBJECTIVES: The objective is to establish a hierarchical training system for nosocomial infection, customized to meet the specific requirements of hospitals at all levels, and evaluate its efficacy. METHODS: A questionnaire survey was conducted among third-party staff members at hospitals of different levels to assess their understanding of nosocomial infection prevention measures. Based on the survey results, a hierarchical training system was developed for nosocomial infection among the participants. After the training, a post-training assessment was carried out to evaluate the participants' comprehension of nosocomial infections. RESULTS: A total of 561 third-party employees participated in the baseline hospital infection knowledge questionnaire. The baseline findings unveiled disparities in the extent to which third-party staff members across various tiers of medical institutions have mastered their knowledge on nosocomial infections. After undergoing hierarchical training, the deficiencies of hospitals at all levels have been rectified, thereby effectively enhancing the level of knowledge regarding nosocomial infections among third-party personnel. The results of multivariate analysis indicate that individuals with limited work experience should enhance their training in medical waste disposal and acquire a deeper understanding of personal protection measures related to nosocomial infections. Moreover, infrequent annual training sessions may impede the comprehension of nosocomial infection among third-party staff. CONCLUSION: The knowledge of hospital infection among third-party staff at all levels of medical institutions exhibits varying deficiencies. Implementing a hierarchical training approach is a meaningful strategy that effectively enhances the level of hospital infection knowledge among these staff members.

  • Analysis Method of Special Physical Training Mode of Basketball Teams in Colleges Based on WeChat Applet and FTTA Optimised LSTM

    Appears in:
    phat 24(1):

    Authors:
    Shuaishuai Zhang, Gang Chen

    Published:
    4th Dec 2024

    Abstract:
    OBJECTIVE: this paper proposes a basketball special physical training mode analysis method based on WeChat applet and optimization algorithm to improve the deep learning network. METHODS: Using the applet data set and the coaches' record data as model input data, the proposed method is used to analyse and thus improve the performance of the basketball team's special physical training pattern. RESULTS: Comparing the analysis effect between FTTA-Attention-LSTM analysis model and LSTM, FTTA-LSTM, FTTA-GRU, FTTA-BiLSTM models, the WeChat mini-program oriented basketball team's special physical fitness training mode analysis index system contains 14 factors affecting the analysis model; in analysing the relationship between the size of FTTA population and Attention-LSTM network hidden layer node number experiments, it was found that the selection of the population size of 80, the number of hidden layer nodes for 90; by analysing the FTTA-Attention-LSTM analysis model and other comparative models, it was found that the analysis accuracy of the FTTA-Attention-LSTM analysis model is the smallest, and the analysis time meets the real-time requirements, controlled within 0.001s. CONCLUSION: In the future, principal component analysis technology can be introduced for feature selection to further achieve intelligence and improve the analysis efficiency of the model.  

  • Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students

    Appears in:
    phat 24(1):

    Authors:
    Jing Wang

    Published:
    4th Dec 2024

    Abstract:
    With the development of Internet of Things (IoT) technology, the use of running APP to analyse college students' physical fitness indicators has gradually become a commonly used sports analysis method. Aiming at the problems of insufficient precision of the running APP usage analysis method, easy to fall into the local optimum, and insufficiently comprehensive evaluation effect, this paper proposes a running APP usage analysis method based on deep learning network for some college students' physical fitness indicators. Firstly, feature vectors are taken from the running APP user behaviour data to analyse the values of college students' physical fitness indicators and construct a mapping model of running APP usage analysis for the effects of college students' physical fitness indicators; then, the BiGRU neural network is improved by using the driver-training heuristic optimisation algorithm to construct a running APP usage analysis model for some of the college students' physical fitness indicators; finally, a mapping model is constructed for the effect of running APP usage analysis for some of the college students' physical fitness indicators by using college student-oriented running APP Finally, the effectiveness and robustness of the DTBO algorithm are compared with the user behaviour dataset of the running APP for college students. Finally, the effectiveness and robustness of the DTBO algorithm are compared using the user behaviour data set of the running app platform for college students.

  • Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology

    Appears in:
    phat 24(1):

    Authors:
    Yunxiang Shang

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: Developing exercise rehabilitation assessment and monitoring systems is essential in rehabilitation medicine. Introduces a sports rehabilitation assessment and monitoring system based on combining Chinese and Western medicine technology, aiming to integrate traditional Chinese and Western medicine theory and modern Western medicine technology to provide more comprehensive and personalized rehabilitation services. Through the systematic integration of technologies, the author is committed to building an efficient and precise rehabilitation system to provide patients with more scientific and practical rehabilitation programs. OBJECTIVE: The research system employs various sensor technologies, including motion capture devices, biosensors, and pulse recognition technology, by combining Chinese and Western medicine. The motion capture device enables real-time monitoring of the patient's movement trajectory, joint mobility, and other physiological indicators; the biosensor collects the patient's physiological data, such as heart rate and blood pressure. Meanwhile, Chinese and Western medicine pulse recognition technology was introduced to obtain pulse information specific to Chinese and Western medicine to provide more comprehensive data support for rehabilitation assessment. Integrating these technologies, a multi-level and multi-dimensional rehabilitation assessment system was established. METHODS: This study aims to improve the accuracy and personalization of the rehabilitation assessment and to tailor a rehabilitation plan more in line with the patient's actual situation. Through the combination of Chinese and Western medicine techniques, it aims to break the single perspective of traditional rehabilitation assessment and make the rehabilitation plan closer to the physiological characteristics and pathological state of the patients as well as the needs of the combination of Chinese and Western medicine in the identification and treatment. RESULTS: The system has achieved remarkable results in practical application. The accurate monitoring of the motion capture device provides a more comprehensive understanding of the patient's motor status and accurately analyzes the rehabilitation progress. At the same time, the data collection of biosensors provides doctors with more detailed physiological information, enabling them to formulate rehabilitation plans more comprehensively. The introduction of combined Chinese and Western medicine pulse recognition technology adds a unique auxiliary diagnostic tool of collaborative Chinese and Western medicine to the rehabilitation assessment and improves the personalized level of the rehabilitation plan. CONCLUSION: The sports rehabilitation assessment and monitoring system combining Chinese and Western medicine technology brings new ideas and methods for developing rehabilitation medicine. By fully utilizing the advantages of modern technology and traditional medical knowledge, a comprehensive and in-depth rehabilitation assessment system was constructed to provide patients with more scientific and caring rehabilitation services. Future research will optimize the system's performance and promote the broad application of integrated Chinese and Western medicine technology in rehabilitation.

  • An Innovative CBR-Enhanced Approach for Skin Cancer Classification using Cascade Forest Model and Convolutional Neural Network with Attention Mechanism

    Appears in:
    phat 24(1):

    Authors:
    Safa Gasmi, Akila DJEBBAR, Hanene Djedi, Hayet Farida Merouani

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: In recent years, skin cancer has emerged as a pressing concern, necessitating advanced diagnostic and classification techniques. OBJECTIVES: This paper introduces an innovative hybrid approach that combines deep learning and machine learning to enhance the retrieval phase of the Case-Based Reasoning (CBR) system for skin cancer classification. METHODS: The proposed approach leverages a Convolutional Neural Network (CNN) with an attention mechanism for feature extraction, which is used to build the case base. Additionally, it uses a modified cascade forest model, augmented with traditional machine learning classifiers for classification. This modified cascade forest model incorporates the XGBoost model in its initial layer to facilitate more effective ensemble learning and bolster predictive performance. Subsequently, in the following layers, it use the random forest model to capitalize on its ability to handle high-dimensional feature spaces and maintain diversity within the ensemble. RESULTS: Rigorous experimentation on the balanced HAM10000 dermoscopic image dataset, employing the Synthetic Minority Oversampling Technique (SMOTE), demonstrates the superiority of the modified cascade forest model in multi-class skin cancer classification. This model consistently achieves the highest metrics, including accuracy (95.40%), precision (95.49%), F1-Score (95.38%), and recall (95.44%). CONCLUSION: This research highlights the efficacy of the proposed model compared to other classifiers, emphasizing the significance of the modified cascade forest model in enhancing the accuracy and reliability of skin cancer classification.

  • A New Deepfake Detection Method Based on Compound Scaling Dual-Stream Attention Network

    Appears in:
    phat 24(1):

    Authors:
    Yunfang Chen , Shuya Wang, Chenjun Du

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: Deepfake technology allows for the overlaying of existing images or videos onto target images or videos. The misuse of this technology has led to increasing complexity in information dissemination on the internet, causing harm to personal and societal public interests.  OBJECTIVES: To reduce the impact and harm of deepfake as much as possible, an efficient deepfake detection method is needed.  METHODS: This paper proposes a deepfake detection method based on a compound scaling dual-stream attention network, which combines a compound scaling module and a dual-stream attention module based on Swin Transformer to detect deepfake videos. In architectural design, we utilize the compound scaling module to extract shallowlevel features from the images and feed them into the deep-level feature extraction layer based on the dual-stream attention module. Finally, the obtained features are passed through a fully connected layer for classification, resulting in the detection outcome.  RESULTS: Experiments on the FF++ dataset demonstrate that the deepfake detection accuracy is 95.62%, which shows its superiority to some extent. CONCLUSION: The method proposed in this paper is feasible and can be used to detect deepfake videos or images.

  • Skin Disease Classification Using CNN Algorithms

    Appears in:
    phat 23(1):

    Authors:
    None

    Downloads:
    1313

    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:
    569

    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:
    522

    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:
    468

    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.

  • 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:
    450

    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.

  • 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:
    425

    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.

  • 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:
    419

    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:
    418

    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.

  • 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:
    397

    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.

  • A remote consultation system for sports injury based on wireless sensor network

    Appears in:
    phat 22(31): 4

    Authors:
    Ting Yang, Hongming Guo

    Downloads:
    342

    Abstract:
    INTRODUCTION: Although current research methods can realize the effective collection of human physiological signals in the health monitoring system, they cannot obtain the ideal detection effect due to the influence of the communication performance in the health monitoring system. OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed. METHODS: The wearable sensors is used in the body area network to collect human physiological signals. Through the wireless sensor network of the wireless communication module, the collected human physiological signals are transmitted to the remote consultation module. The wireless communication module selects CC2530 chip as the core chip of the wireless communication module. A fixed partition routing algorithm based on energy balance is used to stably transmit human physiological signals. RESULTS: The consultation personnel of the remote consultation module make a sports injury consultation judgment based on the received physiological signal results of the human body. The system test results show that the designed system can accurately monitor various physiological indicators of the human body. The wireless sensor network energy consumption of the system in this paper is all less than 500J, the energy consumption variance of the cluster head is less than 4×10-3, and the number of surviving nodes can be guaranteed to be higher than 130. It has high communication performance of wireless sensor network. CONCLUSION: The system can accurately judge whether there is a sports injury according to the monitoring results of physiological indicators, and realize the effective consultation of sports injury.
Publisher
EAI
ISSN
2411-7145
Number of Volumes
10
Last Published
2024-01-15
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