EAI Endorsed Transactions on Pervasive Health and Technology https://publications.eai.eu/index.php/phat <p>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. Authors are not charged for article submission and processing.</p> en-US <p>This is an open access article distributed under the terms of the <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.</p> publications@eai.eu (EAI Publications Department) publications@eai.eu (EAI Support) Wed, 27 Jul 2022 08:58:31 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 The associations between mental health and environmental factors in New Zealand: A region-based analytical study https://publications.eai.eu/index.php/phat/article/view/789 <p>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.<br />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.<br />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.<br />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.<br />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.</p> Morten Viehoff, Daniel Grossman, Leona Huang, Jianwei Jiang, Pan Zheng Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/phat/article/view/789 Thu, 05 May 2022 00:00:00 +0000 E-appointments at primary care physicians during Covid-19 pandemic: viewpoint and satisfaction of medical consumers https://publications.eai.eu/index.php/phat/article/view/1129 <p class="ICST-abstracttext"><span lang="EN-GB">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.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">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.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">METHODS: A survey on MC above the age of 15 years was conducted in July 2020. The results were analysed in SPSS 23.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">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.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">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.</span></p> Viktor Denkovski, Goce Gavrilov Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/phat/article/view/1129 Mon, 23 May 2022 00:00:00 +0000 Remote medical video region tamper detection system based on Wireless Sensor Network https://publications.eai.eu/index.php/phat/article/view/702 <p>INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper.</p><p>OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video.</p><p>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.</p><p>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%.</p><p>CONCLUSION: The proposed detection system for remote medical video based on wireless sensor network can better meet the requirements of region tamper detection.</p> Sujuan Li, Shichen Huang Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/phat/article/view/702 Tue, 26 Jul 2022 00:00:00 +0000 A remote consultation system for sports injury based on wireless sensor network https://publications.eai.eu/index.php/phat/article/view/701 <p>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.</p><p>OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed.</p><p>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.</p><p>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.</p><p>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.</p> Hongming Guo, Ting Yang Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/phat/article/view/701 Wed, 27 Jul 2022 00:00:00 +0000 Remote consultation image stitching method based on wireless sensor technology and mathematical morphology https://publications.eai.eu/index.php/phat/article/view/700 <p>INTRODUCTION: In order to obtain seamless and high-precision remote consultation image mosaic results, a remote consultation image mosaic method based on wireless sensor technology and mathematical morphology is studied. A consultation image acquisition unit based on wireless sensor technology is designed, and the remote consultation image signal is collected by sensor; In the process of signal conditioning, a filter based on mathematical morphology is used to reduce the influence of noise on the accuracy of remote consultation image acquisition.</p><p>OBJECTIVES: Compressed sensing technology is used to realize the compression, transmission and recovery of consultation image sampling data.</p><p>METHODS: After preprocessing the image through shadow correction, surf algorithm is used to construct the scale space to determine the main direction of feature points in the image; The extended surf descriptor is constructed based on feature points for consultation image registration.</p><p>RESULTS: Based on the spatial transformation relationship between images, the improved gradual in and gradual out stitching method is used to complete the remote consultation image stitching. Experimental results show that this method can accurately collect consultation image signals, and the corresponding rate of the feature point extraction results reaches nearly 99%, which is relatively robust.</p><p>CONCLUSION: The RMSE error of the image registration results is less than 2.692, which improves the accuracy of the remote consultation image stitching results, well solves the problem of image visual field reduction, and there is no seam in the stitching area.</p> Xiaoge Li Copyright (c) 2022 EAI Endorsed Transactions on Pervasive Health and Technology https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/phat/article/view/700 Wed, 27 Jul 2022 00:00:00 +0000