249 research outputs found

    Can pervasive sensing address current challenges in global healthcare?

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    Important challenges facing global healthcare include the increase in the number of people affected by escalating healthcare costs, chronic and infectious diseases, the need for better and more affordable elderly care and expanding urbanisation combined with air and water pollution. Recent advances in pervasive sensing technologies have led to miniaturised sensor networks that can be worn or integrated within the living environment without affecting a person's daily patterns. These sensors promise to change healthcare from snapshot measurements of physiological parameters to continuous monitoring enabling clinicians to provide guidance on a daily basis. This article surveys several of the solutions provided by these sensor platforms from elderly care to neonatal monitoring and environmental mapping. Some of the opportunities available and the challenges facing the adoption of such technologies in large-scale epidemiological studies are also discussed

    Highly-efficient fog-based deep learning AAL fall detection system

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    [EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research and innovation program as part of the ACTIVAGE project under Grant 732679.Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185S11911“World Population Ageing.” [Online]. Available: http://www.un.org/esa/population/publications/worldageing19502050/. [Accessed: 23-Sep-2018].“Falls, ” World Health Organization. [Online]. Available: http://www.who.int/news-room/fact-sheets/detail/falls. [Accessed: 20-Sep-2018].Rashidi, P., & Mihailidis, A. (2013). A Survey on Ambient-Assisted Living Tools for Older Adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579-590. doi:10.1109/jbhi.2012.2234129Bousquet, J., Kuh, D., Bewick, M., Strandberg, T., Farrell, J., Pengelly, R., … Bringer, J. (2015). Operative definition of active and healthy ageing (AHA): Meeting report. Montpellier October 20–21, 2014. European Geriatric Medicine, 6(2), 196-200. doi:10.1016/j.eurger.2014.12.006“WHO | What is Healthy Ageing?”[Online]. Available: http://www.who.int/ageing/healthy-ageing/en/. [Accessed: 19-Sep-2018].Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., … Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Generation Computer Systems, 90, 435-450. doi:10.1016/j.future.2018.06.042W. Zaremba, “Recurrent neural network regularization,” no. 2013, pp. 1–8, 2015.Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” pp. 1–9, 2014.N. Zerrouki, F. Harrou, Y. Sun, and A. Houacine, “Vision-based human action classification,” vol. 18, no. 12, pp. 5115–5121, 2018.Panahi, L., & Ghods, V. (2018). Human fall detection using machine vision techniques on RGB–D images. Biomedical Signal Processing and Control, 44, 146-153. doi:10.1016/j.bspc.2018.04.014Y. Li, K.C. Ho, and M. Popescu, “A microphone array system for automatic fall detection,” vol. 59, no. 2, pp. 1291–1301, 2012.Taramasco, C., Rodenas, T., Martinez, F., Fuentes, P., Munoz, R., Olivares, R., … Demongeot, J. (2018). A Novel Monitoring System for Fall Detection in Older People. IEEE Access, 6, 43563-43574. doi:10.1109/access.2018.2861331C. Wang et al., “Low-power fall detector using triaxial accelerometry and barometric pressure sensing,” vol. 12, no. 6, pp. 2302–2311, 2016.S.B. Khojasteh and E. De Cal, “Improving fall detection using an on-wrist wearable accelerometer,” pp. 1–28.Theodoridis, T., Solachidis, V., Vretos, N., & Daras, P. (2017). Human Fall Detection from Acceleration Measurements Using a Recurrent Neural Network. IFMBE Proceedings, 145-149. doi:10.1007/978-981-10-7419-6_25F. Sposaro and G. Tyson, “iFall : an android application for fall monitoring and response,” pp. 6119–6122, 2009.A. Ngu, Y. Wu, H. Zare, A.P. B, B. Yarbrough, and L. Yao, “Fall detection using smartwatch sensor data with accessor architecture,” vol. 2, pp. 81–93.P. Jantaraprim and P. Phukpattaranont, “Fall detection for the elderly using a support vector machine,” no. 1, pp. 484–490, 2012.Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinovitch, S. N. (2016). A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing, 55(1), 45-55. doi:10.1007/s11517-016-1504-yV. Carletti, A. Greco, A. Saggese, and M. Vento, “A smartphone-based system for detecting falls using anomaly detection,” vol. 6978, 2017, pp. 490–499.Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and Big Data. Procedia Computer Science, 130, 603-610. doi:10.1016/j.procs.2018.04.11

    Analysis of Android Device-Based Solutions for Fall Detection

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    Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de EconomĂ­a y Competitividad TEC2013-42711-

    Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

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    Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

    Adopting Modern Fitness Sensors to Improve Patient Care

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    Technology found in modern fitness sensor devices advances at a very fast pace and current smartwatches are on the verge of closing the gap between being an everyday object and a medically reliable monitoring device. In this thesis, the possibility of adopting fitness sensor devices in medical environments is explored and use cases in which sensor devices can be deployed are examined. Their successful transfer from the area of sports to medical analyses and treatments may help patients to deal with their illnesses and to improve the level of patient care found today. Privacy and security issues as well as social concerns associated with such a disruptive evolution are discussed and practical tests of a pulse oximeter in various activities of daily living are conducted. The collected health data depicts a close representation of the performed activities. Furthermore, three types of fitness sensor devices were used in different real-life scenarios and the resulting data is compared. The results show that the recorded vital signs may differ significantly, depending on the scenario. ii

    Data Analysis for Physical Activity Monitoring

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    Master's thesis in Computer SciencePhysical activity is essential for humans for maintaining a healthy and comfortable lifestyle. With science and technological advancements, there comes various guidelines for the amount of physical activity a person should perform. Monitoring the physical activity enables us to follow those guidelines and be aware of own activity. Wearable computing is allowing us to track and monitor our own performed physical activities by mostly intrinsic (minimal) interaction. Physical activity monitoring is an emerging research area in wearable computing. Our thesis is about identifying and classifying which activity is being performed. We have used various classifiers and evaluation metrics to validate our classifier models

    IS THERE A GRIÄŚ ATTRACTOR?

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    In this study the time series of monthly averages of temperature, cloudiness and solar radiation are analyzed to investigate the chaotic properties of the local climate, and the possibility of the estimation of an minimum number of independent variables necessary to model the time evolution of the underlying climate system. The analysis of observations is based on a statistical procedure, called "correlation algorithm", leading to the measures of dimensionality or degrees of freedom which control the underlying dynamics. The results show that there is no indication of the existence of a lowdimensional attractor of the Zagreb-GriÄŤ time series. Within a climate system, it is possible to isolate a three-variable subsystem, controlling the large amplitudes of the local climate. These are: temperature, cloudiness and solar radiation. The existence of autocorrelation in the time series, due to solar radiation, has enabled the detection of a low-dimensional attractor. When the seasonal cycle has been theoretically filtered out of the series, the correlation disapped, as well as the low-dimensional climate subsystem

    Soft Continuum Robotic Airbag Integrated with Passive Walker for Fall Mitigation

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    This thesis describes the prototype and development of a soft continuum robotic airbag system that is attached to a passive mobility walker. The system can deploy in multiple configurations: to the front, left, or right of the walker depending on the direction of a detected fall. The continuum component of the project is made of nylon fabric with thin cables, allowing it to be compactly stored before deploying. The airbag is inflated in real time during a fall using a novel compression system. Results of experiments with the prototype in each configuration are presented. The system deploys consistently across falls, significantly reducing the g-force of impact

    A Solder-Defined Computer Architecture for Backdoor and Malware Resistance

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    This research is about securing control of those devices we most depend on for integrity and confidentiality. An emerging concern is that complex integrated circuits may be subject to exploitable defects or backdoors, and measures for inspection and audit of these chips are neither supported nor scalable. One approach for providing a “supply chain firewall” may be to forgo such components, and instead to build central processing units (CPUs) and other complex logic from simple, generic parts. This work investigates the capability and speed ceiling when open-source hardware methodologies are fused with maker-scale assembly tools and visible-scale final inspection. The author has designed, and demonstrated in simulation, a 36-bit CPU and protected memory subsystem that use only synchronous static random access memory (SRAM) and trivial glue logic integrated circuits as components. The design presently lacks preemptive multitasking, ability to load firmware into the SRAMs used as logic elements, and input/output. Strategies are presented for adding these missing subsystems, again using only SRAM and trivial glue logic. A load-store architecture is employed with four clock cycles per instruction. Simulations indicate that a clock speed of at least 64 MHz is probable, corresponding to 16 million instructions per second (16 MIPS), despite the architecture containing no microprocessors, field programmable gate arrays, programmable logic devices, application specific integrated circuits, or other purchased complex logic. The lower speed, larger size, higher power consumption, and higher cost of an “SRAM minicomputer,” compared to traditional microcontrollers, may be offset by the fully open architecture—hardware and firmware—along with more rigorous user control, reliability, transparency, and auditability of the system. SRAM logic is also particularly well suited for building arithmetic logic units, and can implement complex operations such as population count, a hash function for associative arrays, or a pseudorandom number generator with good statistical properties in as few as eight clock cycles per 36-bit word processed. 36-bit unsigned multiplication can be implemented in software in 47 instructions or fewer (188 clock cycles). A general theory is developed for fast SRAM parallel multipliers should they be needed
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