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One of the main challenges in ubiquitous computing is mak- ing users interact with computing appliances in an easy and natural manner. In this paper we discuss how to turn or- dinary devices into Smart Products that are more intuitive to use and are self-explanatory. We present a general archi- tecture and a distributed runtime environment for buil...
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... hardware of the enhanced system consists of the cof- fee machine, two RFID readers to identify cups, one reader for reading the digital keys, and a PC running the control software. Figure 4 shows the hardware architecture and the individual components. The roles of the individual compo- nents are as follows. ...
Citations
... By being "directly or indirectly digitally augmented and connected" to the processing environment and to the events of the real world through real-time mapping systems, they have access rights to objects, ambient resources, and devices to act jointly and exchange personal information [17,18,27,[30][31][32]. Their improved capabilities expose multiple, new, and complex functions, whether provided by the physical embodiment of communication functionality or by manufacturers of third-party providers, that provide use-value, perceptual qualities, better functionality, or services that produce useful results through activities that make them hyperfunctional or multifunctional [18,30,31,[33][34][35][36][37][38][39]. They extend what users can do with the technology by offering higher usage behavior, enriching themselves with digital functionality, and "connecting to external services and exploiting other objects' capabilities" [27,[40][41][42]. ...
... They communicate with the environment effectively to create an "optimal relationship between users and themselves" [19, 31, 34-36, 38, 45, 49, 51-54]. They collect, provide, and process information by sensing, logging, and interpreting information generated within themselves and around the neighboring external world in which they are situated [28,29,36,[55][56][57][58]. They can process real-time information and collect and broadcast related context information, their users, and functionality automatically and transparently by "computing situational context from sensor data" [31,44,45,50,52,59]. ...
... Human Behavior and Emerging Technologies information about themselves with other digital artifacts or applications" [30,36,39,49,60,61]. The proactivity, adaptivity, awareness, autonomy, anticipation, flexibility, and self-explanatory characteristics are grouped under the definitive category of the definition of the smartness map. ...
The domestication of smart artifacts has transformed our homes into hybrid environments of physical and digital worlds. It also has been changing our mindsets, behaviors, meaning attributions to, expectations from, frustrations about, and interactions with smart artifacts. By extension, the smartness definition is reconstructed by users who are the subject of smart artifact experiences. The current study is aimed at uncovering the user experience of smart artifacts with a focus on cognitive and emotional aspects to better understand what users expect when an artifact is identified as “smart.” Therefore, an online research study is conducted to gain insight into the user experience of smart artifacts from content-rich reviews on e-commerce websites. Robot vacuum cleaners, smartwatches, and smart speakers were chosen as exemplary smart artifacts of the study. Because they offer different types of interaction with distinct aspects, our findings indicate that smartness is associated with trust in expertise, emotional engagement, exaggerated evaluation, and intriguing existence concepts about Emotional UX. In Cognitive UX, smartness relates to reducing mental workload, gratifying experience, perceived phenotype, reciprocal acquaintance, trust-building experience, tailored situatedness, shaping sociality, physical competency, and dual enhancement concepts. These findings demonstrate the potential of conceptualization in the early stages of smart artifact design processes.
... The user"s actions are detected using sensors. The authors experimented with an interface that did not deploy any visual or voice syntax; instead, it directs with machine movement actions [10]. However, machine movements sometimes increase the cognitive load of users if they aren"t accustomed to the flow of machines. ...
User Interface (UI) acts as a mediator between human and computer or any other system or sub-systems which exhibit its function, structure, and behavior. The complexities of UI are open issues that make it formidable to explore it. Supervision makes any task easier by explaining the elements and intentions of UI. Self-Explanatory User Interfaces (SEUI) traps the complexities of UI and provides assistive tools with it in order to supervise users addressing usability using visual and aural syntax. UIs require adapting to the mental model of users and support them during their task. The study presented in this paper has been administered to adult users (age group: 50-64 years) with minimum exposure to technologies and often requires support to get accustomed to interfaces. To complete a task successfully, it is essential to justify the intent of the design, purpose of any textual entry, the usability of the buttons, importance of the labels, visibility, or blurriness of certain items to end-users. Literature associated with SEUI highlight the intervention of Model-Driven Engineering in UI. But very few studies in this area have investigated concatenation of visual and aural syntax in UI. The visual aspect aims at explaining a clear and concise diagrammatic representation of the hierarchy of UI and the significance of each user entry while the aural dimension intends to provide audio instructions to the users. The results include evidence of significant growth in task completion rate and improvement in the task completion time of the novice users assisted with combined visual and aural syntax tool embedded in an interface. The results of the current study are significant in light of the designing and development of UIs that address adults who are a novice and lack exposure to UIs. The solution presented in this paper would support them to migrate from being a novice to an expert.
... Within the Smart Movement [35][ 36] products have been increasingly equipped with electronics, enabling the assessment of isolated environmental data and the interpretation of basic contextual information (e.g., wearable activity trackers, smartphones and watches, etc.). However, these products typically have deterministic and predefined behavior and lack the capabilities required for sustainable and autonomous human-like cognitive functions, such as perception, awareness, learning, reasoning and decision-making. ...
Future commercial products and product assemblies could greatly benefit from recent developments in machine learning , providing the foundation of cognitive products equipped with sensors and actuators and embedded into tangible objects operating in the real world. This paper identifies key challenges in the related fields and provides motivation for further advancements particularly in the domain of resource-constrained distributed and embedded Artificial Intelligence. Enabling cognitive capabilities , such as perception, reasoning, learning and planning, could result in higher reliability, adaptivity and improved performance, however it would require an increased involvement of non-technical disciplines like cognitive neuroscience. We propose a generic top-level cognitive architecture providing a reference to various research areas involved in this multifaceted field. Conceptual prototypes of two cognitive products, targeting real-world industrial environments, are presented and discussed. Keywords-cognitive systems; ambient intelligence; embedded systems ; distributed intelligence; cognitive components. I. INTRODUCTION Humans have developed skills to survive in a complex world by evolving adequate information processing mechanisms well suited to deal with ill-structured problems involving a high degree of uncertainty. The human brain, however, cannot compete with machines on tasks requiring massive computational resources. Machines are faster, more accurate and stronger than humans. However, humans outperform machines in many tasks, which require flexible, reliable and adaptive control. Since these abilities are currently beyond the reach of state-of-the-art Artificial Intelligence (AI), much of the inspiration for implementing future intelligent machines needs to be taken from cognitive sciences that study computational models of human perception, attention and motor control. The ultimate goal is to turn machines into ones that can reason using substantial amount of appropriately represented knowledge, learn from its past experiences in order to continuously improve performance, be aware of its own capabilities, reflect on its own behavior and respond robustly to surprise [1]. Such a high level intelligence should be complemented by low level cognitive abilities provided by reactive models. This would enable a major leap in the quality of interaction and cooperation with humans.
... In recent years an increasing number of smart products are being developed [1]- [5]. Such smart products are able to collect, process and produce information and they can make use of knowledge about themselves, their users and their context [1], [2]. These smart products are often combined with context-aware services to form a smart product-service system [3]. ...
... However, developing these products and services is not an easy task [2]- [5]. It is difficult for the designers to know beforehand how a system can best be used or how it will be used in reality [2]. ...
... However, developing these products and services is not an easy task [2]- [5]. It is difficult for the designers to know beforehand how a system can best be used or how it will be used in reality [2]. Yet, a thorough understanding of the users and the environment is necessary to develop smart productservice systems [3]. ...
... While Smart Items show a rather passive behavior, i.e., giving goods identity, monitoring, or logging, Smart Products have more self-awareness and can actively interact with their surroundings. They are real-world objects, devices or software services bundled with knowledge about themselves, others, and their embedding [2]. They are motivated by the increased complexity of technical products today. ...
The Internet of Things (IoT) and the Internet of Services (IoS) are two well-known exemplars of the emerging 'Internet variants'. These variants will be tightly interwoven yet specific with respect to the supporting technologies needed. The present paper discusses the five variants identified as essential by the authors: IoT, IoS, Internet-of-Humans, Internet-of-Crowds, and Internet-of-Clouds. For each variant, a non-comprehensive set of research challenges is cited and related to the state of the art and to ongoing projects of the lab.
... Algunos ejemplos de interacción, como se ha indicado antes, sería el de añadirle inteligencia a algunos objetos cotidianos para facilitarnos las tareas. Por ejemplo, se le podría añadir RFID a una cafetera [10] para poder controlarla de forma remota y saber el estado en el que se encuentra. Con RFID se pueden detectar las acciones que realizan los usuarios y automáticamente comenzar procesos según el usuario que sea. ...
Internet de las cosas (IoT en sus siglas en inglés) es un concepto que apareció hace unos
años y que, como veremos, integra el mundo virtual de la información con el mundo real de
las cosas. Explicaremos qué son las cosas, qué son los objetos inteligentes, qué ámbitos pueden
verse afectados por ellos y cómo interactuar con este tipo de objetos.
También indicaremos el alcance de esta nueva tecnología, veremos en qué entornos se
está aplicando y algunas de las tecnologías relacionadas, como son RFID, los códigos QR y
NFC.
... Aitenbichler et al. [4] summarize the idea of Smart Products by stating that "Smart Products are real-world objects, devices, or software services bundled with knowledge about themselves, others, and their embedding". This definition sets the focus on the knowledge dimension and emphasizes the capability of autonomous behavior depending on context information. ...
... Third, sufficient computing power is required to execute smart behavior. To achieve smart behavior with limited computational resources, a number of research programs and initiatives have recently been started, which majorly focus on three aspects [4][6] [40]: a first technology-oriented research stream explores the application of semantic modeling of context, product behavior, and interaction. A second stream is concerned with new techniques for superior human-computer interaction as not all smart products will be able to include conventional screen-based user interfaces. ...
... For instance, RFID-based Electronic Product Codes (EPC) attached to products allow the retrieval of associated product information that can be used for differentiation [12]. Products that actively use information services for adaptations to situations, users and other products are denoted as smart products [13,14]. They are claimed to be situated, personalized, adaptive, pro-active, business-aware and networkcapable [4]. ...
Mobile technologies have the potential to change not only brick-and-mortar stores but also the way, how customers interact with physical products. They enable operational agility by means of improved availability and quality of information required by customers for in-store purchase decisions. In this paper, we show how an in-store bundling scenario can be supported by semantically enriched products (denoted as smart products) that provide dynamic product information through the use of mobile recommendation agents (MRA). We introduce therefore the concept of knowledge-based bundling that relies on smart products and MRA. In addition, we developed a MRA and evaluated its user acceptance for product bundle purchases. For this purpose, a lab experiment was conducted (n=37), which resulted in some design enhancements and promising adoption rates.
... Another issue discussed was about the modality in which the interaction should happen. Again, the particular scenarios in which the interaction takes place defines the more appropriate channel: while users of a smart coffee machine [3] would quickly adopt and get used to a voice interface, in other scenarios as the one proposed by CoRA (shopping women underwear) discretion was of outmost importance and therefore visual interaction through a personal, trusted device was preferred. ...
... About its users, based on elaborate user models that take into account dynamically changing user knowledge (learning/forgetting) and distinguish the different user categories reflected in the lifecycle plus each individual user herself. This short reference to own work shall be sufficient for the scope of this introductory paper, for a next level of detail the reader may refer to [1]. ...
Sophisticated commercial products and product assemblies can greatly benefit from novel IT-based approaches to the conditioning
of these products and of ‘product knowledge’, leading to what we call Smart Products. The paper motivates the need for such
novel approaches, introduces important relevant challenges and research domains, and provides an early definition of Smart
Products.
