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This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible. The current state-of-the-art in emotion regulation through music and colour/light is implemented with the final goal of enhancing the quality of life and care of the subject. The paper describes the three main parts of the architecture, namely "Emotion Detection", "Emotion Regulation" and "Emotion Feedback Control". "Emotion Detection" works with the data captured from the patient, whereas "Emotion Regulation" offers him/her different musical pieces and colour/light settings. "Emotion Feedback Control" performs as a feedback control loop to assess the effect of emotion regulation over emotion detection. We are currently testing the overall architecture and the intervention in real environments to achieve our final goal.

This paper introduces a software framework denominated ARISTARKO. ARISTARKO has been designed and developed to integrate the data acquisition from a wearable physiological data acquisition device with any necessary processing layers in order to classify the acquired signals into a predefined set of emotional states. In this particular article, we use ARISTARKO for the sake of designing an experiment capable of showing a series of images from the well-known IAPS database. The IAPS pictures are labelled with arousal, valence and dominance values. Arousal is classified from the information contained in the database and the physiological signals acquired by the wearable, namely electro-dermal, electrocardiogram and superficial electromyogram activity, and skin temperature.

The increasing life expectancy is causing a fast ageing population around the globe, which is raising the demand on assistive systems based on ambient intelligence. While numerous papers have focused on the physical aspects in elderly, only a few works have attempted to regulate their emotional state. In this work, a new approach for monitoring and detecting the emotional state in elderly is presented. First, different physiological signals are acquired by means of wearable sensors, and data are transmitted to the embedded system. Next, noise and artifacts are removed by applying different signal processing techniques, depending on the signal behavior. Finally, several temporal and statistical markers are extracted and used to feed the classification model. In this very first version, a logistic regression model is used to detect two possible emotional states. In order to calibrate the model and adjust the boundary decision, twenty volunteers have agreed to be monitored and recorded to train the model. Finally, a decision maker regulates the environment, acting directly upon the elderly’s emotional state.

In this chapter, we describe our work on the design of an auto-managed system for the tracking and location of products in transportation routes, called Transportation Monitoring System (TMSystem). Manufacturers, retailers and customers require tracking of goods in production and distribution lines. Automatic Vehicle Location (AVL) Systems are being introduced in many cities around the world. These systems are aimed for cost reduction purposes, and also provide optimization of time and resources. Companies usually control the quality of their production during the manufacturing phase, but products can also be controlled along the distribution and transportation phases, before they are delivered to customers. When controlling all the phases, including also the location of the transport, a large amount of data (Big Data) will be generated, and should be processed in order to get useful information, and so all the resources in the company are optimized. A four-layer system is proposed in order to provide an efficient solution for the Real-Time Monitoring (RTM) of goods. Two Web Services are proposed, Location Web Service and Check Order Web Service, so that customers can easily access information about the shipment of their orders. Finally, a Web Application is developed to access those Web Services.

In this paper we summarize our work on the design of an auto-managed system for the tracking and location of products in transportation routes. Manufacturers, retailers and customers require tracking of goods in production and distribution lines. Companies usually control the quality of their production during the manufacturing phase, but products can also be controlled along the distribution and transportation phases, before they are delivered to customers. A service-oriented four-layer system is proposed in order to provide an efficient solution for the Real-Time Monitoring (RTM) of goods. Several Web Services are defined, so that customers can easily access information about the shipment of their orders. Finally, a sample Web Application is developed to access those Web Services.

This paper summarizes our work on the design and implementation of a RFID-based system for the tracking and location of objects, and urban transport routes as a particular case. The data retrieved from the RFID readers can be associated with the data stored in a database, so we propose the development of a Web environment to gather information through any web browser. The architectural model is based on Fosstrak EPCISRepository, as it is an open source RFID software platform that implements the EPC Network specifications and it has been certified by EPCGlobal. We have developed a WebServiceapplication for location of the bus stops and lines on the route map, as a particular case of the tracking system, and the time left for the bus to arrive to a specific stop is also indicated on the graphical interface.