Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

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). World Health Organization. 2011. ISBN: 9789241564250Lin, C., Mobile telemedicine: A survey study. J Med Syst April 36(2):511–520, 2012.El Khaddar, M.A., Harroud, H., Boulmalf, M., Elkoutbi, M., Habbani, A., Emerging wireless technologies in e-health Trends, challenges, and framework design issues. 2012 International Conference on Multimedia Computing and Systems (ICMCS). 440–445, 2012.Luanrattana, R., Win, K. T., Fulcher, J., and Iverson, D., Mobile technology use in medical education. J Med Syst 36(1):113–122, 2012.Yang, S. C., Mobile applications and 4 G wireless networks: A framework for analysis. Campus-Wide Information Systems 29(5):344–357, 2012.Kumar, B., Singh, S.P., Mohan, A., Emerging mobile communication technologies for health. 2010 International Conference on Computer and Communication Technology, ICCCT-2010; Allahabad; pp. 828–832, 2010.Yan, H., Huo, H., Xu, Y., and Gidlund, M., Wireless sensor network based E-health system—implementation and experimental results. IEEE Transactions on Consumer Electronics 56(4):2288–2295, 2010.IDC (2013) Press release: Strong demand for smartphones and heated vendor competition characterize the worldwide mobile phone market at the end of 2012. http://www.idc.com/getdoc.jsp?containerId=prUS23916413#.UVBKiRdhWCn . Accessed 11 September 2013.IDC (2012) IDC Raises its worldwide tablet forecast on continued strong demand and forthcoming new product launches. http://www.idc.com/getdoc.jsp?containerId=prUS23696912#.US9x86JhWCl . Accessed 11 September 2013.International Data Corporation (2013) Android and iOS combine for 91.1 % of the worldwide smartphone OS market in 4Q12 and 87.6 % for the year. http://www.idc.com/getdoc.jsp?containerId=prUS23946013 . Accessed 11 September 2013.Jones, C., (2013) Apple and Google continue to gain US Smartphone market share. Forbes. http://www.forbes.com/sites/chuckjones/2013/01/04/apple-and-google-continue-to-gain-us-smartphone-market-share/ . 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 . Accessed 17 September 2013.QxMD Medical Software Inc. (2013) Calculate by QxMD. Google play. https://play.google.com/store/apps/details?id=com.qxmd.calculate . Accessed 17 September 2013.Skyscape (2012) ACC pocket guides. Google play. https://play.google.com/store/apps/details?id=com.skyscape.packagefiveepkthreeundata.android.voucher.ui . Accessed 17 September 2013.Skyscape (2013) Skyscape medical resources. Google play. https://play.google.com/store/apps/details?id=com.skyscape.android.ui&hl=en . Accessed 17 September 2013.Pieter Kubben, M.D., (2012) NeuroMind. Google play. https://play.google.com/store/apps/details?id=eu.dign.NeuroMind . Accessed 17 September 2013.Mobile Systems, Inc. (2013) 2013 Medical diagnosis TR. Google play. https://play.google.com/store/apps/details?id=com.mobisystems.msdict.embedded.wireless.mcgrawhill.cmdt2013 . Accessed 17 September 2013.World Health Organization (2013) The global burden of disease: 2004 update. http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf . Accessed 18 September 2013.Martínez-Pérez, B., de la Torre-Díez, I., Candelas-Plasencia, S., and López-Coronado, M., Development and evaluation of tools for measuring the Quality of Experience (QoE) in mHealth applications. J Med Syst 37(5):9976, 2013

An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care

Technology in Parkinson's disease:challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society

Human activity detection based on mobile devices

Aquesta tesi se centra en la detecció d'activitat humana a partir de dispositius mòbils i portàtils. Escollim Hexiwear com el nostre dispositiu portàtil per recollir les dades de l'activitat humana diària, com ara l'acceleració de tres eixos, l'orientació de tres eixos, la velocitat angular i la posició de tres eixos. Aquest projecte consisteix en el desenvolupament d'una aplicació per a telèfon intel·ligent per a l'usuari en l'anàlisi de dades, la visualització de dades i la generació de resultats. L'objectiu és construir un prototip obert i modular que pugui servir d'exemple o plantilla per al desenvolupament d'altres projectes. L'aplicació està desenvolupada amb JAVA per Android Studio. L'aplicació permet a l'usuari connectar-se amb el dispositiu portàtil i reconèixer la seva activitat diària. Per a l'algorisme de classificació de l'activitat diària, hem utilitzat dos mètodes diferents, el primer és mitjançant l'establiment de diferents llindars, el segon és mitjançant l'aprenentatge automàtic. L'aplicació es va provar i els resultats van ser satisfactoris, ja que l'aplicació generada va funcionar correctament. Malgrat les òbvies limitacions, la feina feta és un punt de partida per a desenvolupaments futurs。Esta tesis se centra en la detección de actividad humana basada en dispositivos móviles y portátiles. Elegimos Hexiwear como nuestro dispositivo portátil para recopilar los datos de la actividad humana diaria, como la aceleración de tres ejes, la orientación de tres ejes, la velocidad angular de tres ejes y la posición. Este proyecto implica la creación de una aplicación de teléfono para usuarios de análisis de datos, visualización de datos y generación de resultados. El objetivo es construir un prototipo abierto y modular que pueda servir como ejemplo o plantilla para el desarrollo de otros proyectos. La aplicación está desarrollada usando JAVA por Android Studio. La aplicación permite al usuario conectarse con el dispositivo portátil y reconocer su actividad diaria. Para el algoritmo de clasificación de la actividad diaria, usamos dos métodos diferentes, el primero es establecer umbrales diferentes, el segundo es usar el aprendizaje automático. La aplicación fue probada y los resultados fueron satisfactorios, ya que la aplicación generada funcionó correctamente. A pesar de las limitaciones evidentes, el trabajo realizado es un punto de partida para futuros desarrollos.  This thesis focuses on human activity detection based on mobile and wearable devices. We choose Hexiwear as our wearable device to collect the human daily activity data, like tri-axis acceleration, tri-axis orientation, tri-axis angular velocity and position. This project consists in the development of a smartphone application for the user in data analysis， data visualization and generates results. The objective is to build an open and modular prototype that can serve as an example or template for the development of other projects. The application is developed using JAVA by Android Studio. The application allows the user to connect with the wearable device, and recognize their daily activity. For the daily activity classify algorithm, we used two different methods, the first one is by set different thresholds, the second is by using the machine learning. The application was tested and the results were satisfactory, as the generated application worked properly. Despite the obvious limitations, the work done is a starting point for future developments

Wearable feedback systems for rehabilitation

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine

A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases.Comment: 20 pages, 6 tables, 3 figure

Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft