Aujourd'hui, il y a une grande variété d'applications mobiles de premiers secours pour les appareils Android et iOS pour aider les utilisateurs en situations dangereuses. Cependant, leur utilisation massive est limitée en raison de plusieurs aspects, tels que l'interaction complexe pour l'utilisateur ou le besoin d'appareils spéciaux pour l'identification médicale. Pour trouver une solution au problème décrit précédemment, nous adoptons une approche basée sur les interactions proxémiques. Dans ce contexte, nous proposons une premier Application de secours mobile (FAMA) basée sur l'interaction Proxémique, développé avec Bluetooth à basse consommation (BLE) et la balise (Beacon) technologie. Dans la première évaluation, nous décrivons les aspects et les défis liés au développement de FAMA.Ces résultats démontrent l'avantage de l'utilisation de l'interaction proxémique pour mettre en oeuvre l'IHM, dans le contexte de l'urgence et des services de secours, en termes de meilleure performance et d'accessibilité, par rapport aux applications mobiles de premiers soins existantes. Mots ClésInteractions proxémiques; beacon; applications mobiles.

Currently, mobile technologies are present in our daily day in different activities and their use keeps increasing. In this context, proxemic interaction, derived from proxemic theory, is becoming an influential approach to implement Mobile Human-Computer Interaction (MobileHCI) in smart environments, based on five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO). The existing tools for implementing proxemic applications require fixed devices that make it difficult to built mobile proxemic apps. This work aims to propose a framework for the development of proxemic applications for smart environments comprised by entities, whose interactions are supported by MobileHCI defined in terms of DILMO dimensions. Our proposed framework allows defining and managing all components in a smart proxemic environment. The framework also provides an API, that allows developers to simplify the process of proxemic information sensing (i.e., detection of DILMO dimensions) from mobile phones and wearable sensors. We demonstrate and evaluate the effectiveness and suitability of our framework, through the proof-of-concept which describes the implementation of two proxemic mobile applications built in a context-based infrastructure for smart proxemic environments based on mobile devices.

Currently, people's daily lives are affected by the pandemic produced by the COVID-19. One of the main problem is the quickly and easy spreading of the virus. Healthcare workers are affected by nosocomial infections (also called as hospital-acquired infections) that exist in workplaces and more specifically, from health care equipment. In practice, the use of technology is quite common in health care settings. However, due to the touchability of mobile digital devices, their use contributes to nosocomial infections, according to several studies. Some applications based on tracking people have been implemented in order to facilitate Human-Computer Interaction (HCI) and preventing contamination of surfaces by people's hands. Notwithstanding, their use still presents limitations related to implementing applications to be used in some hospital environments, such as care rooms, laboratories, clinical workrooms. To overcome these limitations, we propose the use of interpersonal distances and proxemic dimensions (i.e., Distance, Identity, Location, Movement, and Orientation -DILMO) for implementing HCI with mobile devices that reduces their touchability. The aim is to facilitate the development of mobile apps with proxemic HCI, supported in a proposed architecture, to stop spreading of nosocomial infection of COVID-19 and others. To show the usability and suitability of our proposal, we present two prototypes of apps for mobile devices as proof-of concept, using several combination of proxemic DILMO dimensions to model proxemic HCI that allow flexibility in interpersonal and devices people interactions.

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