Development of Arduino Basic Learning Kit in Robotics for Beginner Level Learning

This Basic Arduino Learning Kit in Robotics is developed to help someone who wants to learn the basic concepts of Arduino and its operation in the field of robotics which is becoming increasingly important in our daily lives. In this learning kit, it provides three practical works that allow students to deepen their knowledge of the Arduino Uno microcontroller in robotics step by step. In Practical Work 1, students are exposed to the experiment of lighting three LED lights with pattern of running lights and flashing lights. In Practical Work 2, students are exposed to measurement work using ultrasonic detector and displaying its reading on a computer screen and on the screen of a mobile phone with the Android operating system via a Bluetooth module. In Practical Work 3, students are shown how to build a three -wheeled prototype robot and how to control the robot using a mobile phone with the Android operating system. The effectiveness of this learning kit has been tested in a learning workshop on 21 Pre-Diploma students of the Session of June 2020 Polytechnic of Sultan Mizan Zainal Abidin and has shown very encouraging feedback

Portable spirometer using pressure-volume method with Bluetooth integration to Android smartphone

This paper presents a study on an embedded spirometer using the low-cost MPX5100DP pressure sensor and an Arduino Uno board to measure the air exhaled flow rate and calculate force vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and the FEV1/FVC ratio of human lungs volume. The exhaled air flow rate was measured from differential pressure in the sections of a mouthpiece tube using the venturi effect equation. This constructed mouthpiece and the embedded spirometer resulted in a 96.27% FVC reading accuracy with a deviation of 0.09 L and 98.05% FEV1 accuracy with a deviation of 0.05 L compared to spirometry. This spirometer integrates an HC-05 Bluetooth module for spirometry data transceiving to a smartphone for display and recording in an Android application for further chronic obstructive pulmonary disease (COPD) diagnosis

Investigation of Effective Classification Method for Online Health Service Recommendation System

Hospital Recommendation Services have been gaining popularity these days. There are many applications and systems that are recommending hospitals based on the user’s requirements and to meet the patient satisfaction. These applications take the reviews of the patients and the users and based on these reviews, they recommend the hospitals. Also if a person is new to the location that he is currently residing, when the speciality is given as input by him, then these applications recommend the hospitals. But the problem is that everyone is not aware of the medical terms like specialities. For those people, “Health Service Recommendation System” comes handy. “Health Service Recommendation System” is an Android Application for finding hospitals within a specified range of distance and requirements provided by the client using the Naïve Bayes classification algorithm. Naïve Bayes algorithm classifies the speciality and thus helps in achieving the maximum accuracy compared to the other algorithms used. This application is helpful even for the people who are not aware of the specialities of the hospitals

Investigation of Effective Classification Method for Online Health Service Recommendation System

Hospital Recommendation Services have been gaining popularity these days. There are many applications and systems that are recommending hospitals based on the user’s requirements and to meet the patient satisfaction. These applications take the reviews of the patients and the users and based on these reviews, they recommend the hospitals. Also if a person is new to the location that he is currently residing, when the speciality is given as input by him, then these applications recommend the hospitals. But the problem is that everyone is not aware of the medical terms like specialities. For those people, “Health Service Recommendation System” comes handy. “Health Service Recommendation System” is an Android Application for finding hospitals within a specified range of distance and requirements provided by the client using the Naïve Bayes classification algorithm. Naïve Bayes algorithm classifies the speciality and thus helps in achieving the maximum accuracy compared to the other algorithms used. This application is helpful even for the people who are not aware of the specialities of the hospitals

Mobile clinical decision support systems and applications: a literature and commercial review

The final publication is available at Springer via http://dx.doi.org/10.1007/s10916-013-0004-y[EN] Background: The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. Objective: The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Methods: Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Results: 92 relevant papers and 192 commercial apps were found. 44 papers were focused only on mobile clinical decision support systems. 171 apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. Conclusions: The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.This research has been partially supported by Ministerio de Economía y Competitividad, Spain. This research has been partially supported by the ICT-248765 EU-FP7 Project. This research has been partially supported by the IPT-2011-1126-900000 project under the INNPACTO 2011 program, Ministerio de Ciencia e Innovación.Martínez Pérez, B.; De La Torre Diez, I.; López Coronado, M.; Sainz De Abajo, B.; Robles Viejo, M.; García Gómez, JM. (2014). Mobile clinical decision support systems and applications: a literature and commercial review. Journal of Medical Systems. 38(1):1-10. https://doi.org/10.1007/s10916-013-0004-yS110381Van De Belt, T. H., Engelen, L. J., Berben, S. A., and Schoonhoven, L., Definition of Health 2.0 and Medicine 2.0: A systematic review. J Med Internet Res 2010:12(2), 2012.Oh, H., Rizo, C., Enkin, M., and Jadad, A., What is eHealth (3): A systematic review of published definitions. J Med Internet Res 7(1):1, 2005. PMID: 15829471.World Health Organization (2011) mHealth: New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth (Global Observatory for eHealth Series, Volume 3). 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Accessed 11 September 2013.Apple (2013) iTunes. http://www.apple.com/itunes/ . Accessed 11 September 2013.Google (2013) Google play. https://play.google.com/store . Accessed 11 September 2013.Rowinski, D., (2013) The data doesn’t lie: iOS apps are better than android. Readwrite mobile. http://readwrite.com/2013/01/30/the-data-doesnt-lie-ios-apps-are-better-quality-than-android . Accessed 11 September 2013.Rajan, S. P., and Rajamony, S., Viable investigations and real-time recitation of enhanced ECG-based cardiac telemonitoring system for homecare applications: A systematic evaluation. Telemed J E Health 19(4):278–286, 2013.Logan, A. G., Transforming hypertension management using mobile health technology for telemonitoring and self-care support. Can J Cardiol 29(5):579–585, 2013.Tamrat, T., and Kachnowski, S., Special delivery: An analysis of mHealth in maternal and newborn health programs and their outcomes around the world. Matern Child Health J 16(5):1092–1101, 2012.Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., and Herreros-González, J., Mobile Apps in Cardiology: Review. JMIR Mhealth Uhealth 1(2):e15, 2013.de Wit HA, Mestres Gonzalvo C, Hurkens KP, Mulder WJ, Janknegt R, et al., Development of a computer system to support medication reviews in nursing homes. Int J Clin Pharm. 26, 2013.Dahlström, O., Thyberg, I., Hass, U., Skogh, T., and Timpka, T., Designing a decision support system for existing clinical organizational structures: Considerations from a rheumatology clinic. J Med Syst 30(5):325–31, 2006.Lambin P, Roelofs E, Reymen B, Velazquez ER, Buijsen J, et al., ‘Rapid learning health care in oncology’ - An approach towards decision support systems enabling customised radiotherapy’. Radiother Oncol. 27, 2013.Graham, T. A., Bullard, M. J., Kushniruk, A. W., Holroyd, B. R., and Rowe, B. H., Assessing the sensibility of two clinical decision support systems. J Med Syst 32(5):361–8, 2008.Martínez-Pérez, B., de la Torre-Díez, I., and López-Coronado, M., Mobile health applications for the most prevalent conditions by the World Health Organization: Review and analysis. J Med Internet Res 15(6):e120, 2013.Savel, T. G., Lee, B. A., Ledbetter, G., Brown, S., LaValley, D., et al., PTT advisor: A CDC-supported initiative to develop a mobile clinical laboratory decision support application for the iOS platform. Online J Public Health Inform 5(2):215, 2013.Doctor Doctor Inc. (2009) iDoc. iTunes. https://itunes.apple.com/es/app/idoc/id328354734?mt=8 . Accessed 13 September 2013.Hardyman, W., Bullock, A., Brown, A., Carter-Ingram, S., and Stacey, M., Mobile technology supporting trainee doctors’ workplace learning and patient care: An evaluation. BMC Med Educ 13:6, 2013.Lee, N. J., Chen, E. S., Currie, L. M., Donovan, M., Hall, E. K., et al., The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters. ANS Adv Nurs Sci 32(3):211–21, 2009.Divall, P., Camosso-Stefinovic, J., and Baker, R., The use of personal digital assistants in clinical decision making by health care professionals: A systematic review. Health Informatics J 19(1):16–28, 2013.Chignell, M, and Yesha, Y, Lo, J., New methods for clinical decision support in hospitals. In Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research (CASCON’10). Toronto, ON; Canada, 2010Charani, E., Kyratsis, Y., Lawson, W., Wickens, H., Brannigan, E. T., et al., An analysis of the development and implementation of a smartphone application for the delivery of antimicrobial prescribing policy: Lessons learnt. J Antimicrob Chemother 68(4):960–7, 2013.Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., et al., Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease. PLoS One 8(2):e56956, 2013.Hervás, R., Fontecha, J., Ausín, D., Castanedo, F., Bravo, J., et al., Mobile monitoring and reasoning methods to prevent cardiovascular diseases. Sensors (Basel) 13(5):6524–41, 2013.Di Noia, T., Ostuni, V. C., Pesce, F., Binetti, G., Naso, N., et al., An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst Appl 40(11):4438–4445, 2013.Velikova, M., van Scheltinga, J. T., Lucas, P. J. F., and Spaanderman, M., Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare. Int J Approx Reason, 2013. doi: 10.1016/j.ijar.2013.03.016 .Medical Data Solutions (2012) Pediatric clinical pathways. Google play. https://play.google.com/store/apps/details?id=com.ipathways . 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Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis

Nowadays e-Health web applications allow doctors to access different types of features, such as knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time, access to a web stethoscope, to a medical image uploader that predicts if a certain disease is present, through deep learning methods, and also allows the visualization of all historical data of a patient. This dissertation has the objective of defending the concept of online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. The process of obtaining vital signs was done via artificial intelligence using a computer camera as sensor. This methodology requires that the user is at a state of rest during the measurements. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.No quotidiano, as aplicações Web e-Saúde permitem aos médicos acesso a diferentes tipos de funcionalidades, como saber qual a medicação que o doente consumiu ou a realização de consultas online. Os sistemas via internet para a saúde podem ser melhorados, utilizando mecanismos de inteligência artificial para os processos de deteção de doenças e de obtenção de dados biológicos, permitindo que os médicos tenham informações importantes que facilitam o processo de diagnóstico ou a escolha do tratamento correto para um determinado utente. O trabalho de investigação proposto pretende apresentar uma abordagem inovadora na comparação com as plataformas tradicionais, ao disponibilizar sinais vitais online em tempo real, acesso a um estetoscópio web, a um uploader de imagens médicas que prevê se uma determinada doença está presente, através de métodos de aprendizagem profunda, bem como permite visualizar todos os dados históricos de um paciente. Esta dissertação visa defender o conceito de consultas virtuais, providenciando funcionalidades complementares aos processos tradicionais de realização de um diagnóstico médico, através da utilização de práticas de engenharia de software. O processo de obtenção de sinais vitais foi feito através de inteligência artificial para visão computacional utilizando uma câmara de computador. Esta metodologia requer que o utilizador esteja em estado de repouso durante a obtenção dos dados medidos. Esta investigação permitiu concluir que, no futuro, muitos processos médicos atuais provavelmente serão feitos online, sendo esta prática considerada extremamente útil na análise e tratamento de doenças contagiosas, ou de casos que requerem acompanhamento constante

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

Background: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. Objective: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. Methods: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. Results: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Conclusions: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain