Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics

[EN] Internet-of-Things (IoT) can allow healthcare professionals to remotely monitor patients by analyzing the sensors outputs with big data analytics. Sleeping conditions are one of the most influential factors on health. However, the literature lacks of the appropriate simulation tools to widely support the research on the recognition of sleeping postures. This paper proposes an agent-based simulation framework to simulate sleeper movements on a simulated smart bed with load sensors. This framework allows one to define sleeping posture recognition algorithms and compare their outcomes with the poses adopted by the sleeper. This novel presented ABS-BedIoT simulator allows users to graphically explore the results with starplots, evolution charts, and final visual representations of the states of the bed sensors. This simulator can also generate logs text files with big data for applying offline big data techniques on them. The source code of ABS-BedIoT and some examples of logs are freely available from a public research repository. The current approach is illustrated with an algorithm that properly recognized the simulated sleeping postures with an average accuracy of 98%. This accuracy is higher than the one reported by an existing alternative work in this area.This work was supported in part by the Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores Program through the Spanish Ministry of Education, Culture and Sport under Grant CAS17/00005, in part by the Universidad de Zaragoza, Fundacion Bancaria Ibercaja, and Fundacion CAI in the Programa Ibercaja-CAI de Estancias de Investigacion under Grant IT24/16, in part by the Desarrollo Colaborativo de Soluciones AAL through the Spanish Ministry of Economy and Competitiveness under Grant TIN2014-57028-R, in part by the Organismo Autonomo Programas Educativos Europeos under Grant 2013-1-CZ1-GRU06-14277, and in part by the Fondo Social Europeo and the Departamento de Tecnologia y Universidad del Gobierno de Aragon for their joint support under Grant Ref-T81.García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2018). Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics. IEEE Access. 6:366-379. https://doi.org/10.1109/ACCESS.2017.2764467S366379

Extending the Design Space of E-textile Assistive Smart Environment Applications

The thriving field of Smart Environments has allowed computing devices to gain new capabilities and develop new interfaces, thus becoming more and more part of our lives. In many of these areas it is unthinkable to renounce to the assisting functionality such as e.g. comfort and safety functions during driving, safety functionality while working in an industrial plant, or self-optimization of daily activities with a Smartwatch. Adults spend a lot of time on flexible surfaces such as in the office chair, in bed or in the car seat. These are crucial parts of our environments. Even though environments have become smarter with integrated computing gaining new capabilities and new interfaces, mostly rigid surfaces and objects have become smarter. In this thesis, I build on the advantages flexible and bendable surfaces have to offer and look into the creation process of assistive Smart Environment applications leveraging these surfaces. I have done this with three main contributions. First, since most Smart Environment applications are built-in into rigid surfaces, I extend the body of knowledge by designing new assistive applications integrated in flexible surfaces such as comfortable chairs, beds, or any type of soft, flexible objects. These developed applications offer assistance e.g. through preventive functionality such as decubitus ulcer prevention while lying in bed, back pain prevention while sitting on a chair or emotion detection while detecting movements on a couch. Second, I propose a new framework for the design process of flexible surface prototypes and its challenges of creating hardware prototypes in multiple iterations, using resources such as work time and material costs. I address this research challenge by creating a simulation framework which can be used to design applications with changing surface shape. In a first step I validate the simulation framework by building a real prototype and a simulated prototype and compare the results in terms of sensor amount and sensor placement. Furthermore, I use this developed simulation framework to analyse the influence it has on an application design if the developer is experienced or not. Finally, since sensor capabilities play a major role during the design process, and humans come often in contact with surfaces made of fabric, I combine the integration advantages of fabric and those of capacitive proximity sensing electrodes. By conducting a multitude of capacitive proximity sensing measurements, I determine the performance of electrodes made by varying properties such as material, shape, size, pattern density, stitching type, or supporting fabric. I discuss the results from this performance evaluation and condense them into e-textile capacitive sensing electrode guidelines, applied exemplary on the use case of creating a bed sheet for breathing rate detection

Multimode optical fiber specklegram smart bed sensor array

Significance: Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes.We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially. Aim: Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed. Approach: Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3 × 4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3 × 4 image is formed by comparing the signal at each crossing point between two fibers. Results: The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing. Conclusions: The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.Stephen C. Warren-Smith, Adam D. Kilpatrick, Kabish Wisal, and Linh V. Nguye

Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference