The evolution of mobile devices has triggered the appearance of intelligent personal assistants (IPAs). IPAs are software agents used to support users to fulfill several daily actions. They are supposed to be intelligent in such a way that allows them to give their owners advices about many different subjects. To do so, IPAs must learn about their user behavior and routines. With the current state of the art technologies, scenarios of ubiquitous communication can be created. One of the potential enablers for those scenarios is the Internet of Things (IoT) paradigm where machines with decision support systems interact and communicate among them. In an IoT environment, IPAs can interact with other smart objects in order to gain new knowledge and awareness about their users. This paper proposes a novel IoT-based mobile gateway solution for mobile health (m-Health) scenarios. This gateway autonomously collects information about the user/patient location, heart rate, and possible fall detection. Moreover, it forwards the collected information to a caretaker IPA, in real time, that will manage a set of actions and alarms appropriately. The algorithms used for each mobile gateway service, and the scenarios where the mobile gateway acts as a communication channel or a smart object are also addressed on this paper.

No abstract

This study aimed to (i) explore the discriminatory power of the task-related variables and the context in establishing differences in the elite futsal leagues of Portugal, Spain, and Russia and (ii) understand how these variables vary according to the match outcome. Methodological issues concerning efficiency (goals and shots), offensive organisation (positional attack, counterattack, set pieces, or 5vs4+Goalkeeper), 1st goal scored during matches (home or away team), match type (balanced or unbalanced), and match outcome (winner, loser, or drawer) were discussed. Archival data were obtained from the 2017–2018 season of Portuguese, Spanish, and Russian professional futsal leagues for all play-off matches. Crosstabs analysis was conducted to establish the significance relationship between the elite futsal leagues and the situational variables. Afterward, discriminant analysis was used to identify the task-related variables that maximise mean differences between different league teams for defining offensive profile, and the variations found when the condition of the winner, loser, or drawer is taken into account. The results allowed to understand that the Portuguese and Russian teams used the positional attacks more, and less the counterattacks and set pieces than the Spaniards, who present a more balanced offensive profile. Overall, winners were better discriminated by goals scored, whereas 5vs4+Goalkeeper strategy discriminated loser teams. Coaches should be aware of these different offensive profiles in order to increase control over the match planning and decrease predictability against opposing teams.

Purpose: to identify the key performance indicators that discriminate all-star from non-all-star players; and to differentiate winning from drawing/losing teams during the Euro Cup 2018 Futsal (Slovenia). Methods: the sample consisted of all matches (n = 20) played by 12 teams (87 players). Differences between both players and teams were calculated using the Mann-Whitney U and student-t tests and the binary logistic regression (assessing the relationship between all-star players or winning teams and several match-and contextually based variables). Results: minutes per match, goals, assists, ball recoveries, % shots on target, % key pass accuracy, and % challenges won discriminated all-star from non-all-star players. However, only minutes per match (OR: 1.329), goals (OR: 13.547), and ball recoveries (OR: 2.136) per time played were determined to differentiate all-star players. Regarding the team analysis, the following variables discriminated winning from losing/drawing teams: goals, assists, % counterattack success, and % set pieces success. However, only goals (OR: 2.035) and % set piece success (OR: 1.076) predicted the match outcome. Conclusions: the current findings can help coaches to a better understanding of which key performance indicators are important out of all data available, contributing to defining priorities when training and managing competition in elite futsal.