Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis

Given the importance of sleep quality for preventing and treating a wide range of diseases, but also considering technological advances in artificial intelligence and smart textiles, the type and number of data recorded during sleep have increased considerably in recent years. Diseases such as Parkinson's, cancer, scoliosis, diabetes, or heart disease can be diagnosed early with wearable sensors. Also, smart textiles, which involve integrating low-energy wearable sensors into clothing, allow analysis of sleep movements, patient position, and number of breaths per minute and can be used to automatically alert the doctor if the patient seems to need clinical care. However, current monitoring solutions are limited in the number of parameters considered to determine sleep quality, generally relying on empirical solutions to rate sleep or to identify a particular disease. To harness the amount of the recorded data and the advances of Artificial Intelligence, this paper proposes a fuzzy logic-based solution for the analysis and determination of a new sleep quality index for the early identification of diseases and reduction of associated risks. For the determination of this new index, the fuzzy system input was taken as values related to room temperature, skin temperature, environmental noise, blood pressure, pulse, VO2max and the number of breaths per minute a patient experiences during sleep. Using a predefined fuzzy rule table, each simulated sleep period was given a score in the range of 1-4, where 1 means totally inadequate sleep and 4 means excellent sleep quality. Recent findings were used to establish the range of variance of the 7 monitored parameters, as evaluated by scientific papers published in prestigious international journals. The system successfully passed the authors' tests and can be further improved by including additional parameters and by using a neural network to assess sleep quality using this new index

System for monitoring and supporting the treatment of sleep apnea using IoT and big data

[EN] Sleep apnea has become in the sleep disorder that causes greater concern in recent years due to its morbidity and mortality, higher medical care costs and poor people quality of life. Some proposals have addressed sleep apnea disease in elderly people, but they have still some technical limitations. For these reasons, this paper presents an innovative system based on fog and cloud computing technologies which in combination with IoT and big data platforms offers new opportunities to build novel and innovative services for supporting the sleep apnea and to overcome the current limitations. Particularly, the system is built on several low-power wireless networks with heterogeneous smart devices (i.e, sensors and actuators). In the fog, an edge node (Smart IoT Gateway) provides IoT connection and interoperability and pre-processing IoT data to detect events in real-time that might endanger the elderly's health and to act accordingly. In the cloud, a Generic Enabler Context Broker manages, stores and injects data into the big data analyzer for further processing and analyzing. The system's performance and subjective applicability are evaluated using over 30 GB size datasets and a questionnaire fulfilled by medicals specialist, respectively. Results show that the system data analytics improve the health professionals' decision making to monitor and guide sleep apnea treatment, as well as improving elderly people's quality of life. (C) 2018 Elsevier B.V. All rights reserved.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 and the Interoperability of Heterogeneous IoT Platforms project (INTER-IoT) under Grant 687283.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive and Mobile Computing. 50:25-40. https://doi.org/10.1016/j.pmcj.2018.07.007S25405

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

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Media use during adolescence: the recommendations of the Italian Pediatric Society.

BACKGROUND: The use of media device, such as smartphone and tablet, is currently increasing, especially among the youngest. Adolescents spend more and more time with their smartphones consulting social media, mainly Facebook, Instagram and Twitter because. Adolescents often feel the necessity to use a media device as a means to construct a social identity and express themselves. For some children, smartphone ownership starts even sooner as young as 7 yrs, according to internet safety experts. MATERIAL AND METHODS: We analyzed the evidence on media use and its consequences in adolescence. RESULTS: In literature, smartphones and tablets use may negatively influences the psychophysical development of the adolescent, such as learning, sleep and sigh. Moreover, obesity, distraction, addiction, cyberbullism and Hikikomori phenomena are described in adolescents who use media device too frequently. The Italian Pediatric Society provide action-oriented recommendations for families and clinicians to avoid negative outcomes. CONCLUSIONS: Both parents and clinicians should be aware of the widespread phenomenon of media device use among adolescents and try to avoid psychophysical consequences on the youngest

Development of Early Social Interactions in Infants Exposed to Artificial Intelligence from Birth

Research suggests that technology density in a home may change interactions parents and infants in the earliest months of life. This study explored how the use of smart baby technology influenced parental perceptions of development and early social interactions. A qualitative, case methodology was used. The participants in this study were one family with newborn twins. Data was collected over a six month period using journals, field notes, and observations. Thematic coding of these materials was used to answer the questions of the study. Results suggest that use of smart technology supported the emerging parenting skills and allowed the parents to confidently establish care interactions