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Thematic issue on human-centred ambient intelligence: cognitive approaches, reasoning and learning
(2017)
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.
Saving energy and road safety became important in the last decades, hence several driving assistant systems were developed that help to improve the driving behaviour. However, these driving systems cover the area of either energy-efficiency or safety. Furthermore, they do not consider the reaction of the driver to a shown recommendation and the driver stress level. In this paper, the decision process of showing a recommendation to the driver in an energy-efficient and safety relevant driving system is presented. The decision process considers the driver's reaction to a shown recommendation and the driver stress in order to increase the user acceptance and the road safety. The results of the evaluation showed that the driving system was able to show recommendations when needed, while suppressing recommendations when the driver ignored a recommendation repeatedly or when the driver was in stress.
To evaluate the quality of sleep, it is important to determine how much time was spent in each sleep stage during the night. The gold standard in this domain is an overnight polysomnography (PSG). But the recording of the necessary electrophysiological signals is extensive and complex and the environment of the sleep laboratory, which is unfamiliar to the patient, might lead to distorted results. In this paper, a sleep stage detection algorithm is proposed that uses only the heart rate signal, derived from electrocardiogram (ECG), as a discriminator. This would make it possible for sleep analysis to be performed at home, saving a lot of effort and money. From the heart rate, using the fast Fourier transformation (FFT), three parameters were calculated in order to distinguish between the different sleep stages. ECG data along with a hypnogram scored by professionals was used from Physionet database, making it easy to compare the results. With an agreement rate of 41.3%, this approach is a good foundation for future research.