Search

In the past few years, within the Home Environment context, the TV set has regained its place together with the Smart Home (SH) concept. In this very dynamic framework, the use of health monitoring devices combined with typical SH appliances and having the TV set as the main interaction point, could allow the entire home environment to monitor the consumer's health status. This paper presents an initial architecture integrating health monitoring devices within the SH environment through a HbbTV - enabled TV set.

Humans' activities are playing a significant role in affecting our planet conditions, accelerating climate change, and wasting our natural resources. Among the actions that can be done to support sustainability and people's awareness about this issue, monitoring environmental conditions and collecting data can be performed thanks to the availability and the adoption of digital and technological solutions, such as Internet of Things infrastructures and deep learning approaches. This paper proposes an approach to support sustainable coastal tourism, which is based on the Social Internet of Things paradigm and on a deep learning-based classifier, with the aim of monitoring and counting people's presence on beaches and coasts. The paper describes a system prototype architecture and a classifier model we have defined, discussing the results obtained in the testing phase we have conducted. The defined model shows interesting and promising results in terms of precision and accuracy.

The proposed solution is related to the use of embedded solutions instead general purpose ones. The tests are been carried in an urban environment in the city of Cagliari, Italy. The proposed solution has been compared with cellular network and Wi-Fi, two of the mainly used general purpose solution in IoT. The parameters used to compare the 3 standards are packet loss and energy consumption. It will be shown how using an embedded solution like SigFox can bring benefits in the parameters listed above. Moreover, the overloading of networks and security aspects are treated in this paper. Thanks to that research, it can be shown in future how an approach of this type could lead to an improvement in resource use and network congestion.

Schizophrenia (SCZ) and bipolar disorder (BD) share some cognitive commonalities. However, the role of associative learning, which is a cornerstone of human cognition mainly relying on hippocampus, has been under-investigated. We assessed behavioral performance during associative learning in a group of SCZ, BD and healthy controls (HC).Nineteen patients with SCZ (36 ± 8.1 years; 13 males, 6 females; all Caucasians), 14 patients with BD (41 ± 9.6 years; 5 males, 9 females; all Caucasians) and 45 HC (27.7 ± 6.9 years; 18 males, 27 females; all Caucasians) were studied. Learning was assessed using an established object-location paired-associative learning paradigm. Subjects learned associations between nine equi-familiar common objects and locations in a nine-location grid. Performance data were analyzed in a repeated measures analysis of variance with time (repeated) and group as factors.Learning curves (performance = (1-e(-k x time)) fitted to average performance data in the three groups revealed lower learning rates in SCZ and BD (k = 0.17 and k = 0.34) than HC (k = 0.78). Significant effects of group (F = 11.05, p0.001) and time (F = 122.06, p0.001) on learning performance were observed.Our study showed that associative learning is impaired in both SCZ and BD, being potentially not affected by medication. Future studies should investigate the neural substrates of learning deficits in SCZ and BD, particularly focusing on hippocampus function and glutamatergic transmission.

Coast erosion is a process that degrades a coastal profile and is mainly due to natural factors (e.g., related to climate change) and overcrowding (e.g., urbanization and massive tourism). While the first cause can be considered as a slow process, the growing presence of humans is leading to rapid aging of coasts. The Mediterranean Sea authorities are focusing on the necessity for a systematic and comprehensive approach to the management of littoral areas. In this context, Italy is searching for a promising solution to safeguard coasts but, at the same time, to manage in an intelligent and “green” way the big amount of tourists. Research communities all over the world indicated the Internet of Things (IoT) as a valid technology to develop solutions in order to try solving or mitigating the coastal erosion problem. IoT-based techniques allow to manage heterogeneous and massive data for real-time monitoring and decision making and can be used for coastal environment and crowd level monitoring. This article presents a monitoring system based on the Social IoT (SIoT), a new paradigm that defines a network where every node is an object capable of establishing the social relationships with other things in an autonomous way according to specific rules. Thanks to social relationships, all involved devices in the monitoring system (i.e., sensors, cameras, and smartphones) are able to collect and exchange information. The proposed system, developed and installed in Cagliari (Italy), is able to evaluate the occupational state of a beach considering environmental and crowding data collected by devices and feedback sent by users.