Green communication for tracking heart rate with smartbands

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user

Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples'' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systemswe present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user''s memory concerning the objects'' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory

Diseño de un sistema ciberfísico aplicado al ámbito de la salud.

Los Sistemas Ciber-Físicos (CPS) tienen la capacidad de coordinar el tratamiento de datos y sistemas de comunicación con el seguimiento y control de las entidades que se encuentran en el entorno físico. Los ejemplos de CPS incluyen sistemas de conducción autónoma, sistemas de monitorización médica, modelos de consumo energético inteligente, sistemas de control de procesos, monitorización de procesos de fabricación, monitorización de infraestructuras y carreteras, sistemas de robótica, domótica y pilotos automáticos aeronáuticos. Estos sistemas están formados por un conjunto de dispositivos como: sensores, unidades de procesamiento, dispositivos de comunicación y en muchos casos servicios en la nube. Debido a la naturaleza de los distintos tipos de hardware un CPS puede estar formado por múltiples dispositivos con diferentes arquitecturas, protocolos e interfaces y en una gran parte de los casos los CPS son sistemas híbridos y distribuidos.Esta tesis presenta el diseño de un CPS centrado en el entorno de la salud. El CPS consta de un armario inteligente de cocina. Este armario posee internamente una placa de procesamiento Raspberry PI que se encarga de recopilar las aperturas y los cierres que se efectúen en este. La función principal del armario es estimar síntomas de aparición de enfermedades neurodegenerativas como el Alzheimer.La tesis también presenta otros elementos en la elaboración de un CPS como medidas de seguridad para evitar ataques de denegación de servicios (DoS) de tal manera que el normal funcionamiento no se vea comprometido. Otro elemento que se tiene en cuenta es la manera de interactuar con el usuario, el cual es objeto de estudio según el tipo de usuario que esté usando el CPS. Finalmente se propone un modelo de consumo energético, el cual se basa en la rutina del usuario para efectuar un consumo de energía en los momentos más álgidos del día en relación a la rutina diaria del usuario.El CPS fue evaluado en un entorno acondicionado y controlado con 23 participantes voluntarios. La fase de evaluación se realizó en 2 pasos en los cuales los voluntarios tuvieron que memorizar y extraer alimentos según el examinador iba indicando, en cada paso se usaron 15 tipos de alimentos distintos. Una vez que se realizaron todas las extracciones cada voluntario tuvo que realizar un test bien conocido de caras y nombres. En este test los participantes deben recordar una cara asociada a un determinado nombre. Un total de 30 caras y nombres son visionados por los voluntarios de manera que cada pareja debe ser memorizado. La etapa final del test de caras y nombres consistió en preguntar a qué cara le pertenece un nombre de 4 posibles. Una vez los resultados de ambos test fueron obtenidos, se contrastaron mediante varios test estadísticos. El resultado arrojó una correlación estadísticamente significativa, esto significa que al ser el test de caras y nombres un test bien conocido y comprobado para medir la memoria, el CPS presentado también posee la misma características.Finalmente, en la tesis se expone cómo los componentes interactivos, eléctricos y de seguridad son diseñados y acoplados para obtener el CPS propuesto.Esta Tesis Doctoral se presenta como compendio de publicaciones editadas, de acuerdo al extracto de 25/06/2020 del Consejo de Gobierno de la Universidad de Zaragoza por el que se aprueba el Reglamento Sobre Tesis Doctorales (Título IV, Capítulo III, artículo 20).<br /

An Empirical Evaluation On Vibrotactile Feedback For Wristband System

With the rapid development of mobile computing, wearable wrist-worn is becoming more and more popular. But the current vibrotactile feedback patterns of most wrist-worn devices are too simple to enable effective interaction in nonvisual scenarios. In this paper, we propose the wristband system with four vibrating motors placed in different positions in the wristband, providing multiple vibration patterns to transmit multi-semantic information for users in eyes-free scenarios. However, we just applied five vibrotactile patterns in experiments (positional up and down, horizontal diagonal, clockwise circular, and total vibration) after contrastive analyzing nine patterns in a pilot experiment. The two experiments with the same 12 participants perform the same experimental process in lab and outdoors. According to the experimental results, users can effectively distinguish the five patterns both in lab and outside, with approximately 90% accuracy (except clockwise circular vibration of outside experiment), proving these five vibration patterns can be used to output multi-semantic information. The system can be applied to eyes-free interaction scenarios for wrist-worn devices.Comment: 10 pages

Collaboration of Smart IoT Devices Exemplified With Smart Cupboards

[EN] The variety of smart things connected to Internet hampers the possibility of having a standalone solution for service-centric provisioning in the Internet of Things (IoT). The different features of smart objects in processing capabilities, memory, and size make it difficult for final users to learn the installation and usage of all these devices in collaboration with other IoT objects, hindering the user experience. In this context, we propose a collaboration mechanism for IoT devices based on the multi-agent systems with mobile agents. This paper illustrates the current approach with smart cupboards for potentially tracking memory losses. The user study revealed that users found working products of this approach usable, easy-to-learn and useful, and they agreed that the current approach could provide a high quality of experience not only in the specific case of service-centric IoT devices for tracking memory losses but also in other domains. The learning capability by means of this approach was showed with significant reductions of reaction times and number of errors over the first and second tests with the current approach. System response timesThis work was supported in part by the Dpto. de Innovacion, Investigacion y Universidad del Gobierno de Aragon through the program FEDER Aragon 2014-2020 Construyendo Europa desde Aragon under Grant T49_17R, in part by the University of Zaragoza and the Foundation Ibercaja through the Research Project Construccion de un framework para agilizar el desarrollo de aplicaciones moviles en el ambito de la salud under Grant JIUZ-2017-TEC-03, in part by the Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores Program, Spanish Ministry of Education, Culture and Sport, under Grant CAS17/00005, in part by the Universidad de Zaragoza, Fundacion Bancaria Ibercaja and Fundacion CAI, Programa Ibercaja-CAI de Estancias de Investigacion, under Grant IT24/16 and Grant IT1/18, in part by the Research Project Desarrollo Colaborativo de Soluciones AAL, 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 Ministerio de Economia y Competitividad through the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento, under Grant TIN2017-84802-C2-1-P.García-Magariño, I.; González-Landero, F.; Amariglio, R.; Lloret, J. (2019). Collaboration of Smart IoT Devices Exemplified With Smart Cupboards. IEEE Access. 7:9881-9892. https://doi.org/10.1109/ACCESS.2018.2890393S98819892

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables

This paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.Agencia Estatal de Investigación | Ref. TIN2016-80515-RUniversidade de Vig

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0