AEVUM: Personalized Health Monitoring System

Advancement in the field of sensors and other portable technologies have resulted in a bevy of health monitoring devices such as blue-tooth and Wi-Fi enabled weighing scales and wearables which help individuals monitor their personal health. This collected information provides a plethora of data points over intervals of time that a primary care physician can utilize to gain a holistic understanding of an individual’s health and provide a more effective and personalized treatment. A drawback of the existing health monitoring devices is that they are not integrated with the professional medical infrastructure. With the wealth of information collected, it is also not feasible for a physician to look through all the data to obtain relevant information or patterns from multiple health monitoring systems. Therefore, it would be beneficial to have a single platform of hardware devices to monitor and collect data and a software application to securely store the collected information, identify patterns for analysis, and summarize the data for the physician and the patient. The aim of this study was to design and develop an unobtrusive, user friendly system, Aevum, which would integrate technology, adapt itself to changes in consumer behavior and integrate with the existing healthcare infrastructure to help an individual monitor their health in a customized manner. Aevum is a multi-device system consisting of a smart, puck-shaped hardware product, a wristband and a software application available to the patient as well as the physician. In addition to monitoring vitals such as heart rate, blood pressure, body temperature and weight, Aevum can monitor environmental factors that affect an individual’s health and uses personalized metrics such as precise calorie intake and medication management to monitor health. This allows the user to personalize Aevum based on their health condition. Finally, Aevum identifies patterns of anomalies in the collected data and compiles the information which can be accessed by the physician to assist in their treatment

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator

Advanced Internet of Things for Personalised Healthcare System: A Survey

As a new revolution of the Internet, Internet of Things (IoT) is rapidly gaining ground as a new research topic in many academic and industrial disciplines, especially in healthcare. Remarkably, due to the rapid proliferation of wearable devices and smartphone, the Internet of Things enabled technology is evolving healthcare from conventional hub based system to more personalised healthcare system (PHS). However, empowering the utility of advanced IoT technology in PHS is still significantly challenging in the area considering many issues, like shortage of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, multi-dimensionality of data generated and high demand for interoperability. In an effect to understand advance of IoT technologies in PHS, this paper will give a systematic review on advanced IoT enabled PHS. It will review the current research of IoT enabled PHS, and key enabling technologies, major IoT enabled applications and successful case studies in healthcare, and finally point out future research trends and challenges

Process mining methodology for health process tracking using real-time indoor location systems

[EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia.Fernández Llatas, C.; Lizondo, A.; Montón Sánchez, E.; Benedí Ruiz, JM.; Traver Salcedo, V. (2015). Process mining methodology for health process tracking using real-time indoor location systems. Sensors. 12:29821-29840. https://doi.org/10.3390/s151229769S298212984012Weske, M., van der Aalst, W. M. P., & Verbeek, H. M. W. (2004). Advances in business process management. Data & Knowledge Engineering, 50(1), 1-8. doi:10.1016/j.datak.2004.01.001Davidoff, F., Haynes, B., Sackett, D., & Smith, R. (1995). Evidence based medicine. BMJ, 310(6987), 1085-1086. doi:10.1136/bmj.310.6987.1085Reilly, B. M. (2004). The essence of EBM. 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DMW - Deutsche Medizinische Wochenschrift, 131(17), 962-967. doi:10.1055/s-2006-939876Zannini, L., Cattaneo, C., Peduzzi, P., Lopiccoli, S., & Auxilia, F. (2012). Experimenting clinical pathways in general practice: a focus group investigation with Italian general practitioners. Journal of Public Health Research, 1(2), 30. doi:10.4081/jphr.2012.e30Rubin, H. R. (2001). The advantages and disadvantages of process-based measures of health care quality. International Journal for Quality in Health Care, 13(6), 469-474. doi:10.1093/intqhc/13.6.469Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080. doi:10.1109/tsmcc.2007.905750Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. 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Internet of Things: Architecture and Services for Healthcare

Internet of Things (IoT) is a recent prominent collaboration of various technologies that enables spatially distributed devices (“things”) to sense, communicate and share information, thus generating a variety of applications and services in Healthcare. IoT is implemented in multiple domains like Smart city, energy and smart grid, Smart home, weather forecasting, Agriculture, Market and Transportation, Manufacturing and testing industries, Healthcare and many more. IoT serves the purpose of making tasks more efficient and productive and at the same time ensuring quality and reliability. IoT technologies provide an enabling framework for inter-connecting devices, systems, and services that go beyond Machine-to-Machine scenarios within today’s internet infrastructure. Healthcare industry is among the fastest fields to embrace IoT for numerous health services. IoT technologies will enable doctors / physicians / caretakers to be in touch with patients all the time. Various physiological parameters and markers can be monitored on a real-time basis for early detection of serious health symptoms that could endanger the life of patients. Diagnosis of diseases can be more accurate and in time for early treatment which will significantly improve recovery time. Diagnostic medical devices, sensors, and imaging devices that are integrated within the network for building an efficient and real-time system. The market for IoT in the healthcare sector is expected to grow rapidly in terms of connecting hospitals with patients for remote monitoring, emergency care services and remote surgery through augmented virtual reality. This thesis explores advances in IoT- based technologies in the healthcare environment. The thesis presents an architecture that defines a possible reference platform for seamless inter-connectivity between devices and software systems to enable new services. The architecture has multiple layers each of which performs specific functions to enable the realization of novel healthcare services. The thesis provides a comprehensive comparison between different Short range communication technologies, Mobile communication and Low Power Wide Area (LPWA) technologies. Based upon different scenarios of IoT healthcare services implementation, data computation capabilities provided by various cloud computing models and edge computing models are also discussed. The thesis provides a survey on various healthcare services that are implemented inside (and outside) hospital premises, e.g., remote health monitoring, Ambient Assisted Living among others. The impact of two prominent key technologies: Network Functions Virtualization (NFV) and Software Defined Networks (SDN) has been discussed and showed the benefits of implementing control and management function-especially at the edge network- utilizing SDN/NFV. This provides a flexible approach for deployment of healthcare services in close proximity to computing resources and improves communication control. IoT acknowledges a reliable and secure data exchange in real-time and oriented to improve Quality of Life (QoL). Internet of Things (IoT) serves the purpose of the advance concatenation of devices, systems, and services that go beyond the Machine-to-Machine scenario within today’s internet infrastructure with extended benefits

Desarrollo de un dispositivo wearable para el reconocimiento de actividades de la vida diaria

El objetivo del trabajo consiste en la detección de actividades realizadas por una persona en su vida diaria. Esta detección se basa en un dispositivo PSoC (Programmable System-on-Chip), un circuito de sensado y comunicación Bluetooth Low Energy

Longitudinal measurement of physical activity using a novel automated system to explore early stage functional recovery after stroke

Introduction There is emphasis on increasing patients’ Physical Activity (PA) to reduce disability and promote independent living. Therefore a new computerised system based on real time location technology called the Rehabilitation Mobility Measurement System (RMMS) was developed to overcome limitations of the current activity monitoring methods and measure PA continuously and unobtrusively. The study objectives were to evaluate the psychometric properties of RMMS and to explore early stage functional recovery after stroke in a rehabilitation unit and at home. Methods Each participant wore a radio-frequency identification tag with an in-built motion sensor on their unaffected wrist. Walking-aids and transport equipment were also fitted with tags. All areas accessed by patients were fitted with infra-red room locators. The tags transmitted movement and location signals to a computer having customised software programs for data processing. Descriptive statistics and graphs were used for analysis. Results The RMMS was very reliable (all ICC>0.90) and demonstrated high level of agreement on validation with observational methods. Longitudinal PA was measured successfully in the rehabilitation unit for 52 patients over 64±53 days. Outside of therapy sessions, patients spent 85% of the waking day in their own rooms undertaking limited high level activities (15%).The average mobility (walking or moving around) was 15 minutes per day only and was strongly correlated with Barthel Index and modified Rivermead Index scores on discharge (spearman’s rho=.-70, p=0.00) accounting for ≥ 43% of variation in these scores. Conclusion RMMS was a reliable and valid tool for measuring mobility; a key factor influencing early stroke recovery. The small amount of time spent active strongly suggests that better organisation of time outside therapy sessions is warranted to maximise daily PA of in-patients. RMMS could be used for motivational feedback for patients and clinicians to ultimately enhance functional activity during rehabilitation in a stroke unit