A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics

[EN] Obtrusive sleep apnea (OSA) is one of the most important sleep disorders because it has a direct adverse impact on the quality of life. Intellectual deterioration, decreased psychomotor performance, behavior, and personality disorders are some of the consequences of OSA. Therefore, a real-time monitoring of this disorder is a critical need in healthcare solutions. There are several systems for OSA detection. Nevertheless, despite their promising results, these systems not guiding their treatment. For these reasons, this research presents an innovative system for both to detect and support of treatment of OSA of elderly people by monitoring multiple factors such as sleep environment, sleep status, physical activities, and physiological parameters as well as the use of open data available in smart cities. Our system architecture performs two types of processing. On the one hand, a pre-processing based on rules that enables the sending of real-time notifications to responsible for the care of elderly, in the event of an emergency situation. This pre-processing is essentially based on a fog computing approach implemented in a smart device operating at the edge of the network that additionally offers advanced interoperability services: technical, syntactic, and semantic. On the other hand, a batch data processing that enables a descriptive analysis that statistically details the behavior of the data and a predictive analysis for the development of services, such as predicting the least polluted place to perform outdoor activities. This processing uses big data tools on cloud computing. The performed experiments show a 93.3% of effectivity in the air quality index prediction to guide the OSA treatment. The system's performance has been evaluated in terms of latency. The achieved results clearly demonstrate that the pre-processing of data at the edge of the network improves the efficiency of the system.This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme through the Interoperability of Heterogeneous IoT Platforms Project (INTER-IoT) under Grant 687283, in part by the Escuela Politecnica Nacional, Ecuador, and in part by the Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics. IEEE Access. 6:35988-36001. https://doi.org/10.1109/ACCESS.2018.2849822S3598836001

A Novel Completely Local Repairable Code Algorithm Based on Erasure Code

Hadoop Distributed File System (HDFS) is widely used in massive data storage. Because of the disadvantage of the multi-copy strategy, the hardware expansion of HDFS cannot keep up with the continuous volume of big data. Now, the traditional data replication strategy has been gradually replaced by Erasure Code due to its smaller redundancy rate and storage overhead. However, compared with replicas, Erasure Code needs to read a certain amount of data blocks during the process of data recovery, resulting in a large amount of overhead for I/O and network. Based on the Reed-Solomon (RS) algorithm, we propose a novel Completely Local Repairable Code (CLRC) algorithm. By grouping RS coded blocks and generating local check blocks, CLRC algorithm can optimize the locality of the RS algorithm, which can reduce the cost of data recovery. Evaluations show that the CLRC algorithm can reduce the bandwidth and I/O consumption during the process of data recovery when a single block is damaged. What\u27s more, the cost of decoding time is only 59% of the RS algorithm

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Big Data em cidades inteligentes: um mapeamento sistemático

O conceito de Cidades Inteligentes ganhou maior atenção nos círculos acadêmicos, industriais e governamentais. À medida que a cidade se desenvolve ao longo do tempo, componentes e subsistemas como redes inteligentes, gerenciamento inteligente de água, tráfego inteligente e sistemas de transporte, sistemas de gerenciamento de resíduos inteligentes, sistemas de segurança inteligentes ou governança eletrônica são adicionados. Esses componentes ingerem e geram uma grande quantidade de dados estruturados, semiestruturados ou não estruturados que podem ser processados usando uma variedade de algoritmos em lotes, microlotes ou em tempo real, visando a melhoria de qualidade de vida dos cidadãos. Esta pesquisa secundária tem como objetivo facilitar a identificação de lacunas neste campo, bem como alinhar o trabalho dos pesquisadores com outros para desenvolver temas de pesquisa mais fortes. Neste estudo, é utilizada a metodologia de pesquisa formal de mapeamento sistemático para fornecer uma revisão abrangente das tecnologias de Big Data na implantação de cidades inteligentes

Energy Efficient Machine Learning-Based Classification of ECG Heartbeat Types

To meet the accuracy, latency and energy efficiency requirements during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Using the state-of-the-art machine learning based classification techniques plays a crucial role in creating the optimal healthcare system on the edge. This thesis first provides a background on the current and emerging edge computing classification techniques for healthcare applications, specifically for electrocardiogram (ECG) beat classification. We then present key findings from an extensive survey of over hundred studies on the topic while taxonomizing the literature with respect to key architectural differences, application areas and requirements. Leveraging the insights drawn from the extensive analysis of the pertinent literature we select a set of most promising machine learning based classification techniques for ECG beats, based on their suitability for implementation on a small edge device called a Raspberry Pi. After implementing these classification techniques on a Raspberry Pi based platform we perform a comparison of the performance of these classification techniques with respect to three key performance indicators (KPI) of interest for health care applications namely accuracy, energy efficiency, and latency. ECG measures the electrical activity of the heart and help healthcare professionals to evaluate heart conditions of a patient, sometimes diagnosing life-threatening conditions. The features of ECG signals are pre-processed and fed into the classification algorithms to detect and classify abnormal beat types. ECG classification requires low complexity but still high level of performance in terms of aforementioned three KPIs. The classification algorithms chosen, namely Naïve Bayes, Multilayer Perceptron (MLP), and distilled deep neural network (DNN) are all energy efficient methods hence suitable for implementation for small edge devices. The comparative multi-faceted evaluation presented in this thesis is a new contribution to research that exists on edge based classification as it offers comparison of selected classification algorithms in terms three KPIs instead of one while using real edge device based implementation. The performance of analyzed machine learning classification techniques is ranked according to each KPI. Benefiting from the results of the comparative analysis presented in this thesis a particular classification algorithm can be selected for optimal deployment in given scenario in healthcare system depending on the specific requirements of the given scenario. Edge computing paves the way for a new generation of health devices that can offer a higher quality of life for users if low-latency, low-energy, and high- performance requirements are addressed

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

[EN] Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.Research presented in this article has been partially funded by Horizon 2020 European Project grant INTER-IoT no. 687283, ACTIVAGE project under grant agreement no. 732679, the Escuela Politecnica Nacional, Ecuador, and Secretaria de Educacion Superior Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.Yacchirema, D.; Suárez De Puga, J.; Palau Salvador, CE.; Esteve Domingo, M. (2019). Fall detection system for elderly people using IoT and ensemble machine learning algorithm. 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