Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

[EN] Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices (p << 0.01) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.Bayo-Monton, JL.; Martinez-Millana, A.; Han, W.; Fernández Llatas, C.; Sun, Y.; Traver Salcedo, V. (2018). Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors. 18(6). https://doi.org/10.3390/s18061851S18

Public Health Innovations Program tailored to Master on Telecommunications’ Students

[EN] Developed and under-developed countries are facing several challenges related to public health and sustainability of health care systems. New challenges demand of the collaborative action of multiple stakeholders with different backgrounds. In the late years, telecommunication engineers are involved in a wide range of companies and institutions to help designing and building innovative and efficient solutions, among which public health is a paradigmatic example. In this paper authors introduce a program for teaching public health principles and tools focused at telecommunications master students. The program is presented in five practices of three hours duration (fifteen hours overall). The sessions are structured in the classic problem-solving methodology in which the students must respond to concrete and general questions by the application of knowledge, practice and reasoning. Each practice includes theoretical framework introduction, provision of tools and use of open repositories to complete the assignments. The covered topics are: mobile health and usability, open data, data mining, Internet of Things and wearable and process mining.Martínez Millana, A.; Martínez Mateu, L.; Guillem Sánchez, MS.; Traver Salcedo, V. (2021). Public Health Innovations Program tailored to Master on Telecommunications’ Students. En Proceedings INNODOCT/20. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 137-144. https://doi.org/10.4995/INN2020.2020.11860OCS13714

Clinical evaluation of stretchable and wearable inkjet-printed strain gauge sensor for respiratory rate monitoring at different body postures

Respiratory rate (RR) is a vital sign with continuous, convenient, and accurate measurement which is difficult and still under investigation. The present study investigates and evaluates a stretchable and wearable inkjet-printed strain gauge sensor (IJP) to estimate the RR continuously by detecting the respiratory volume change in the chest area. As the volume change could cause different strain changes at different body postures, this study aims to investigate the accuracy of the IJP RR sensor at selected postures. The evaluation was performed twice on 15 healthy male subjects (mean ± SD of age: 24 ± 1.22 years). The RR was simultaneously measured in breaths per minute (BPM) by the IJP RR sensor and a reference RR sensor (e-Health nasal thermal sensor) at each of the five body postures namely standing, sitting at 90°, Flower’s position at 45°, supine, and right lateral recumbent. There was no significant difference in measured RR between IJP and reference sensors, between two trials, or between different body postures (all p \u3e 0.05). Body posture did not have any significant effect on the difference of RR measurements between IJP and the reference sensors (difference \u3c 0.01 BPM for each measurement in both trials). The IJP sensor could accurately measure the RR at different body postures, which makes it a promising, simple, and user-friendly option for clinical and daily uses

Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study

Clinical evaluation of stretchable and wearable inkjet-printed strain gauge sensor for respiratory rate monitoring at different measurements locations

The respiration rate (RR) is a vital sign in physiological measurement and clinical diagnosis. RR can be measured using stretchable and wearable strain gauge sensors which detect the respiratory movements in the abdomen or thorax areas caused by volumetric changes. In different body locations, the accuracy of RR detection might differ due to different respiratory movement amplitudes. Few studies have quantitatively investigated the effect of the measurement location on the accuracy of new sensors in RR detection. Using a stretchable and wearable inkjet-printed strain gauge (IPSG) sensor, RR was measured from five body locations (umbilicus, upper abdomen, xiphoid process, upper thorax, and diagonal) on 30 healthy test subjects while sitting on an armless chair. At each location, reference RR was simultaneously detected by the e-Health sensor, and the measurement was repeated twice. Subjects were asked about the comfortableness of locations. Based on Levene’s test, ANOVA was performed to investigate if there is a significant difference in RR between sensors, measurement locations, and two repeated measurements. Bland–Altman analysis was applied to the RR measurements at different locations. The effects of measurement site and measurement trials on RR difference between sensors were also investigated. There was no significant difference between IPSG and reference sensors, between any locations, and between the two measurements (all p > 0.05). As to the RR deviation between IPSG and reference sensors, there was no significant difference between any locations, or between two measurements (all p > 0.05). All the 30 subjects agreed that diagonal and upper thorax positions were the most uncomfortable and most comfortable locations for measurement, respectively. The IPSG sensor could accurately detect RR at five different locations with good repeatability. Upper thorax was the most comfortable location

Evaluation of strategies for the development of efficient code for Raspberry Pi devices

La Internet de las cosas (IO) se enfrenta a desafíos que requieren soluciones ecológicas y paradigmas de eficiencia energética. Las arquitecturas (como el ARM) han evolucionado significativamente en los últimos años, con mejoras en la eficiencia de los procesadores, esenciales para los dispositivos de conexión permanente, como punto focal. Sin embargo, en lo que respecta al software, pocos enfoques analizan las ventajas de escribir un código eficiente al programar dispositivos de IO. Por consiguiente, esta propuesta tiene por objeto mejorar la optimización del código fuente para lograr mejores tiempos de ejecución. Además, se analiza la importancia de diversas técnicas para escribir código eficiente para los dispositivos Pi de Frambuesa, con el objetivo de aumentar la velocidad de ejecución. Se ha desarrollado un conjunto completo de pruebas exclusivamente para analizar y medir las mejoras logradas al aplicar cada una de estas técnicas. De esta manera se toma conciencia del importante impacto que pueden tener las técnicas recomendadas.The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have.• Ministerio de Economía y Competitividad y Fondos FEDER. Proyecto TIN2015-69957-R (I+D+i) • Unión Europea. Programa de Desarrollo Regional Europeo y Programa del Fondo Europeo de Desarrollo (FEDER): Programa Operativo Extremadura 2014-2020. Ref. 2018.14.02.332A.444.00peerReviewe

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach

Low cost autonomous lock-in amplifier for resistance/capacitance sensor measurements

This paper presents the design and experimental characterization of a portable high-precision single-phase lock-in instrument with phase adjustment. The core consists of an analog lock-in amplifier IC prototype, integrated in 0.18 µm CMOS technology with 1.8 V supply, which features programmable gain and operating frequency, resulting in a versatile on-chip solution with power consumption below 834 µW. It incorporates automatic phase alignment of the input and reference signals, performed through both a fixed-90° and a 4-bit digitally programmable phase shifter, specifically designed using commercially available components to operate at 1 kHz frequency. The system is driven by an Arduino YUN board, thus overall conforming a low-cost autonomous signal recovery instrument to determine, in real time, the electrical equivalent of resistive and capacitive sensors with a sensitivity of 16.3 µV/O @ erS < 3 % and 37 kV/F @ erS < 5 %, respectively

Choosing Wearable Internet of Things Devices for Managing Safety in Construction Using Fuzzy Analytic Hierarchy Process as a Decision Support System

Many safety and health risks are faced daily by workers in the field of construction. There is unpredictability and risk embedded in the job and work environment. When compared with other industries, the construction industry has one of the highest numbers of worker injuries, illnesses, fatalities, and near-misses. To eliminate these risky events and make worker performance more predictable, new safety technologies such as the Internet of Things (IoT) and Wearable Sensing Devices (WSD) have been highlighted as effective safety systems. Some of these Wearable Internet of Things (WIoT) and sensory devices are already being used in other industries to observe and collect crucial data for worker safety in the field. However, due to limited information and implementation of these devices in the construction field, Wearable Sensing Devices (WSD) and Internet of Things (IoT) are still relatively underdeveloped and lacking. The main goal of the research is to develop a conceptual decision-making framework that managers and other appropriate personnel can use to select suitable Wearable Internet of Things (WIoT) devices for proper application/ implementation in the construction industry. The research involves a literature review on the aforementioned devices and the development and demonstration of a decision-making framework using the Fuzzy Analytic Hierarchy Process (FAHP)