Journal of Ambient Intelligence and Smart Environments

This editorial presents advances on Human-centred Ambient Intelligence applications which take into account cognitive issues when modelling users (i.e. stress, attention disorders), and learn users’ activities/preferences and adapt to them (i.e. at home, driving a car). These papers also show AmI applications in health and education, which make them even more valuable for the general society.

This paper presents an in-home monitoring system based on WiFi fingerprints for Ambient Assisted Living. WiFi fingerprints are used to continuously locate a patient at the different rooms in her/his home. The experiments performed provide a correctly location rate of 96% in the best case of all studied scenarios. The behavior obtained by location monitoring allows to detect anomalous behavior such as long stays in rooms out of the common schedule. The main characteristics of the presented system are: a) it is robust enough to work without an own WiFi access point, which in turn means a very affordable solution; b) low obtrusiveness, as it is based on the use of a mobile phone; c) highly interoperable with other wireless connections (bluetooth, RFID) present in current mobile phones; d) alarms are triggered when any anomalous behavior is detected.

Smart mobile devices have fostered new interaction scenarios for Ambient Intelligence that demand sophisticated interfaces. The main developers of operating systems for such devices have provided APIs for developers to implement their own applications, including different solutions for developing graphical interfaces, sensor control and voice interaction. Despite the usefulness of such resources, there are no strategies defined for coupling the multimodal interface with the possibilities that the devices offer to identify and adapt to the user needs. This way, current apps are usually developed ad-hoc and the spoken interface is conceived as an input for simple commands. In this paper we present a practical mobile application that integrates features of Android APIs on a modular architecture that emphasizes multimodal conversational interaction and context-awareness to foster user-adaptivity, robustness, and maintainability.

The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In this paper, we present a novel mechanism to predict upcoming values for stress levels based on current and past values for both the driving behavior and environmental factors. First, we analyze the relationship between stress levels and different variables that model the driving behavior (accelerations, decelerations, positive kinetic energy, standard deviation of speed, and road shape). Stress levels are obtained utilizing a Polar H7 heart rate strap. Vehicle telemetry is captured using a smartphone. Second, we study the accuracy of several machine learning algorithms (Support Vector Machine, Multilayer Perceptron, Naïve Bayes, J48, and Deep Belief Network) when used to estimate the stress based on our input data. Finally, an experiment was conducted in a real environment. We considered three different scenarios: home-workplace route, workplace-home route, and driving under heavy traffic. The results show that the proposal can estimate the upcoming stress with high accuracy. This algorithm could be used to develop driving assistants that recommend actions to prevent the stress.

We present the e-Gibalec system, designed to encourage schoolchildren towards a more active lifestyle. The system consists of a mobile application that, through sensors built into the smartphone, detects children’s physical activity and rewards them in a game-like manner. It also consists of a web application that allows the parents and physical education teachers to look at the children’s physical activity history, so they can further motivate them if needed. We discuss the motivational mechanisms employed in the system, provide an evaluation of the accuracy of the activity-recognition component, and present a pilot study that measured the effect of our system on a sample schoolchildren population.

Supporting persons with Down’s Syndrome in their daily activities using ICT is a key element in further advancing their independence and integration into society. The POSEIDON project embraces this goals and develops technology which creates adjustable and personalizable assistive systems. We present a system for Money-Handling Training and assistance for shopping. In this paper we present results of evaluating the Money-Handling Training App in different pilot studies and workshops, with a larger group of persons with Down’s Syndrome, comparing different interaction devices like tablet, personal computer and interactive table. Furthermore, we present evaluation results for the Shopping App.

In traditional learning, teachers can easily have an understanding of how their students work and learn. However, in e-Learning it is more difficult for teachers to monitor how their students behave and learn in the system. As well as to identify if they have any specific awkwardness in their educational process. In this paper, a student model to infer the presence of ADHD symptoms in a Computer-Based Educational System (also known as Learning Management Systems (LMS)) is presented. The student model takes into account two types of students’ characteristics: generic and psychological. Each one is measured through a set of variables, which are correlated to obtain a final profile that can be useful to assist the teaching-learning process. In order to reach this purpose, three Web application tools that collect information about these characteristics have been developed, integrated into a LMS and validated in a case study composed of 30 students (5 suffering from ADHD, 5 that present similar characteristics to ADHD and 20 that supposed do not suffer from ADHD). This case study was carried out through a quantitative research approach and a descriptive scope. Results show that the implemented tools are useful to identify attention problems symptoms in students enrolled in e-learning courses.