Abstract

Abstract
Interactive art implies an active dialogue between the participant and the surrounding space, mediated by a computational system. Reciprocity and recursiveness are key principles to the bidirectional flux of information in this setting, guaranteeing a continuous interaction loop between the participant and the digital system. Viewing the human body as a natural interface, we focus on non-invasive tracking methods for embodiment sensing, such as infra-red depth cameras. Current limitations in participant engagement of interactive artworks in public spaces are introduced and analyzed from the perspective of group dynamics. In this paper we approach Bourriaud's concept of relational aesthetics, relate it to the inherent social context of interactive artwork exhibition, and propose a framework for the development of relational interactive artworks. Interactive art implies an active dialogue between the participant and the surrounding space, mediated by a computational system. Reciprocity and recursiveness are key principles to the bidirectional flux of information in this setting, guaranteeing a continuous interaction loop between the participant and the digital system. Viewing the human body as a natural interface, we focus on non-invasive tracking methods for embodiment sensing, such as infra-red depth cameras. Current limitations in participant engagement of interactive artworks in public spaces are introduced and analyzed from the perspective of group dynamics. In this paper we approach Bourriaud's concept of relational aesthetics and relate it to the inherent social context of interactive artwork exhibition, and propose a framework for the development of relational interactive artworks.

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.

Abstract
Cyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internet-of-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research efforts on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks.

Abstract
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.

Abstract
The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .