Connected Devices

Les neurosciences et l'éducation Gavet nicolas, 2025 L'essor des neurosciences a profondément renouvelé notre compréhension du fonctionnement cérébral. L'éducation, en tant que pratique visant à favoriser l'apprentissage, s'est naturellement tournée vers ces avancées pour éclairer ses méthodes. Mais ce rapprochement entre deux domaines historiquement séparés pose des questions théoriques et pratiques : comment traduire les résultats de la recherche fondamentale en recommandations pédagogiques ? Quels en sont les bénéfices réels ? Quelles précautions faut-il prendre pour éviter les interprétations abusives ? Cet article se propose d'examiner : 1. Les apports des neurosciences à la compréhension des mécanismes de l'apprentissage ; 2. Les applications éducatives validées ; 3. Les limites épistémologiques et pratiques de ce dialogue. 1. Apports des neurosciences à la compréhension des apprentissages 1.1. Plasticité cérébrale

L'Internet des Objets se traduit par une multitude d'objets connectés dotés de fonctions avancées pour l'automatisation et l'assistance aux personnes, soulevant des défis techniques, politiques, sociaux et économiques. L'apprentissage est lié à ces défis en plusieurs sens : si l'apprentissage machine contribue au fonctionnement des objets connectés, ces derniers stimulent en retour l'apprentissage humain en diversifiant les pratiques et les outils. Les méthodes d'apprentissage peuvent s'appuyer sur les récents travaux en neurosciences identifiant les ressorts cognitifs de l'apprentissage : l'attention, l'engagement, le retour d'informations et la consolidation. En outre, l'apprentissage s'est vu modifié par l'évolution technologique : après l'imprimerie, l'informatique et les télécommunications, l'Internet des Objets est un nouveau support d'information pour la transmission des savoirs. À partir de trois dimensions d'analyse (données, interfaces et pervasivité), nous proposons une classification des articulations possibles de l'Internet des Objets pour soutenir l'apprentissage. L'étude de cette relation montre que l'Internet des Objets favorise l'exploration et l'expérimentation, la mise en place de situations authentiques et la contextualisation de l'apprentissage. ABSTRACT. The Internet of Things encompasses a multitude of connected devices with advanced functions for the automation of tasks and assistance to individuals, raising technical, political, social and economic challenges. Learning is linked to these challenges in several ways: the machine learning development contributes to the functioning of connected devices, which in turn stimulate human learning by diversifying practices and tools. Learning methods can lean on recent works in neuroscience that identify cognitive resources for learning, such as attention, engagement, feedback and consolidation. In addition, learning has been modified by technological change: after printing, computing and telecommunications, the Internet of Things is a new information medium for knowledge transmission. Based on three dimensions (data, interfaces and pervasiveness), we propose a classification of possible articulations of the Internet of Things to support learning. The study of this relationship shows that the Internet of Things promotes exploration and experimentation, the setting up of authentic situations and the contextualization of learning. MOTS-CLÉS. Apprentissage, Apprentissage basé sur l'IdO, Internet des Objets, Objets connectés, Sciences cognitives.

Ambient Assisted Living (AAL) represents one of the most promising Internet of Things applications due to its influence on the quality of life and health of the elderly people. However, the interoperability is one of the major issues that needs to be addressed to promote the adoption of AAL solutions in real environments, and to find a way of common exchange between the available connected tools to share the data exchanged. This article will present software buses needs and specify an API based on a MQTT software bus treelike architecture. An example is given to illustrate the efficiency of the API developed in a smart home.

L'objectif de cette communication consiste à montrer de quelle manière les usagers de véhicules électriques utilisent cette innovation et s'en emparent. Il s'agit d'appréhender de quelle façon ces acteurs ressentent et se représentent leurs parcours. Nos résultats montrent comment les outils numériques modifient les représentations des territoires de l'électromobilité. Ce travail de recherche a été réalisé entre 2014 et 2016, sur une aire géographique qui couvre les sites industriels d'un grand constructeur automobile français. Les différentes applications utilisées sont constantes dans la préparation et la découverte de nouveaux parcours. Nous constatons que leur usage s'estompe ensuite en fonction du degré de pratique et de maîtrise territoriale des différents conducteurs interviewés. Nous montrons que le rôle des applications numériques dans les déplacements est déterminant dans les zones où la disponibilité des infrastructures de recharge est inférieure à la demande.

L'Internet des Objets se traduit par une multitude d'objets connectés dotés de fonctions avancées pour l'automatisation et l'assistance aux personnes, soulevant des défis techniques, politiques, sociaux et économiques. L'apprentissage est lié à ces défis en plusieurs sens : si l'apprentissage machine contribue au fonctionnement des objets connectés, ces derniers stimulent en retour l'apprentissage humain en diversifiant les pratiques et les outils. Les méthodes d'apprentissage peuvent s'appuyer sur les récents travaux en neurosciences identifiant les ressorts cognitifs de l'apprentissage : l'attention, l'engagement, le retour d'informations et la consolidation. En outre, l'apprentissage s'est vu modifié par l'évolution technologique : après l'imprimerie, l'informatique et les télécommunications, l'Internet des Objets est un nouveau support d'information pour la transmission des savoirs. À partir de trois dimensions d'analyse (données, interfaces et pervasivité), nous proposons une classification des articulations possibles de l'Internet des Objets pour soutenir l'apprentissage. L'étude de cette relation montre que l'Internet des Objets favorise l'exploration et l'expérimentation, la mise en place de situations authentiques et la contextualisation de l'apprentissage. ABSTRACT. The Internet of Things encompasses a multitude of connected devices with advanced functions for the automation of tasks and assistance to individuals, raising technical, political, social and economic challenges. Learning is linked to these challenges in several ways: the machine learning development contributes to the functioning of connected devices, which in turn stimulate human learning by diversifying practices and tools. Learning methods can lean on recent works in neuroscience that identify cognitive resources for learning, such as attention, engagement, feedback and consolidation. In addition, learning has been modified by technological change: after printing, computing and telecommunications, the Internet of Things is a new information medium for knowledge transmission. Based on three dimensions (data, interfaces and pervasiveness), we propose a classification of possible articulations of the Internet of Things to support learning. The study of this relationship shows that the Internet of Things promotes exploration and experimentation, the setting up of authentic situations and the contextualization of learning. MOTS-CLÉS. Apprentissage, Apprentissage basé sur l'IdO, Internet des Objets, Objets connectés, Sciences cognitives.

Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different ...

Prototyping (iterative loops of design–build–test) is a proven method of efficiently developing new products. Developing products not only quickly, but that are also fit for purpose, implies engaging the end users and iterating the technology at hand. However, there is currently little research on how engineering design can approach developing connected devices. The purpose of this paper is to distinguish and discuss design approaches that are suitable for connected devices. Internet of Things devices consist of both the physical products themselves and the data that is coming out of the products, which we define as the external and internal data, respectively. They both can be prototyped separately, but since the data acquired can influence the design of the device and vice versa, we propose to link these two together in the product development process. This issue becomes more apparent when designing networks of sensors, e.g., for complex artificial intelligence (AI) databases. We explain the principle by describing the development of 1Balance through six different prototypes for human balance measurement. Technologically quantifying balance is an underused approach for objectively evaluating the state of a human's performance. The authors have developed a mobile application for monitoring balance as a physiological signal (amount of sway) via a compact wireless inertial measurement unit (IMU) sensor strapped to the body of the subject for the duration of the measurement. We describe the design process for developing this connected medical device, as well as how the acquired data was used to improve the design of the product. In conclusion, we propose conceptually connecting the external and internal data prototyping loops.

The Internet of Everything is a $19 trillion global opportunity over the next decade: Private-sector firms can create as much as $14.4 trillion of value while cities, governments and other public-sector organizations can create $4.6 trillion.
As we all know, technology is evolving rapidly. It has, and will continue to, profoundly change our lives in the years to come. Just four years ago, it would have been hard to imagine we would have access to the kind information we now have in our pockets. So what will the future look like four years from now? What kind of positive changes can we expect to see rising out of the Internet of Everything (IoE) by 2020?

“Today computers -- and, therefore, the Internet -- are almost wholly dependent on hu-man beings for information. Nearly all of the roughly 50 petabytes (a petabyte is 1,024terabytes) of data available on the Internet were first captured and created by hu-man beings by typing, pressing a record button, taking a digital picture or scanning a bar code.
The problem is, people have limited time, attention and accuracy -- all of which means they are not very good at capturing data about things in the real world. If we had com-puters that knew everything there was to know about things -- using data they gathered without any help from us -- we would be able to track and count everything and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling and whether they were fresh or past their best.”
The broadband divide could prove to be a real hampering force to the internet of things movement that is gaining speed today. Cloud, mobility, big data are all con-verging and making a seamless network, but the success of this convergence depends heavily on the ability to actually move and access the data. And considering that millions of additional devices (some of which are just sensors) will enter the equation means its time for further investment and quick. According to the CIO Survey, organizations are in a prime position to innovate and make significant changes.

Internet of Things (IoT) is entering from concept to commercialization in the industry. The growth of sensors, connected devices, communication technologies and reduction in costs of processing, sensor, and bandwidth technologies are triggering the growth of internet of things industry. By 2020, it is going to surpass 50 billion connected devices in the world and over $1.7 trillion market size (IDC Research). This article highlights the growth of internet of things, drivers, applications, technologies and related industries.