Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest

Aplicación de Redes Neuronales en controladores de baterías

Trabajo de final de Curso presentado al Instituto Latino-Americano de Tecnología, Infraestructura y Territorio de la Universidad Federal de Integración Latinoamericana, como requisito para obtener el título de Bachiller en Ingeniería de Energías. Orientador:Dr. Jorge Javier Gimenez Ledesma y Coorientador:Dr. Oswaldo Hideo Ando JuniorEl presente estudio tiene como objetivo aplicar el uso de redes neuronales artificiales, que tiene como una de sus aplicaciones operar como un aproximador universal de funciones, mapeando la relación funcional entre las variables de un sistema a partir de un conjunto conocido de valores muestreados. En este contexto, este trabajo aborda un método para predecir el estado de carga de las baterías utilizando técnicas de redes neuronales artificiales a través de una base de datos y modelos de la curva de carga de las baterías de cloruro de sodio y níquel; y así analizar el comportamiento del sistema de gestión de baterías, a través de los modelos encontrados en las curvas de salida. Así este estudio en principio presenta una breve introducción del mercado de energía, seguido de la justificativa y motivación que llevaron a desarrollar el mismo. En seguida, se presenta la metodología empleada, en el software MATLAB, paso a paso para la obtención de las curvas de carga. Seguido de una breve descripciones de los sistemas de almacenamiento de energía, baterías. Continuando con una descripción del sistema de gerenciamiento de baterías, funcionalidades y aplicaciones. Y por fin, una descripción de redes neuronales, clasificación arquitectura y aplicaciones en ingeniería; que, para el caso, el método propuestos utiliza una red neuronal artificial Perceptron multicapa, una arquitectura de avance (feedforward) con algoritmo de entrenamiento de retropropagación. Con todo esto, finalmente, los resultados indican la capacidad del método para indicar el estado de carga de la batería, así como el análisis de los errores estipulados. Concluyendo que, la configuración utilizada tiene un mejor rendimiento al ajustar el número de capas, y puede ser aplicado en otras baterías, como es el caso de la batería de litio; con los errores y percances encontrados a lo largo de este estudio se presenta algunos trabajos a futuro.The present study aims to apply the use of artificial neural networks, which has as one of its applications to operate as a universal approximator of functions, mapping the functional relationship between the variables of a system from a known set of sampled values. In this context, this work addresses a method to predict the state of charge of batteries using artificialneural network techniques through a database and models of the charge curve of batteries of sodium chloride and nickel; and thus analyze the behavior of the battery management system, through the models found in the output curves. Thus, this study in principle presents a brief introduction to the energy market, followed by the justification and motivation that led to its development. Next, the methodology used is presented, in the MATLAB software, step by step to obtain the load curves. Followed by a briefdescription of the energy storage systems, batteries. Continuing with a description of the battery management system, features and applications. And finally, a description of neural networks, architectural classification and applications in engineering; that, for that matter, the proposed method uses a multilayer Perceptron artificial neural network, a feedforward architecture with backpropagation training algorithm. With all this, finally, the results indicate the ability of the method to indicate the state of charge of the battery, as well as the analysis of the stipulated errors. Concluding that, the configuration used has a better performance when adjusting the number of layers, and can be applied in other batteries, as in the case of the lithium battery; With the errors and mishaps found throughout this study, some future work is presented.Fundação Parque Tecnológico de Itaipu - Laboratório Bateria

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective