Towards interactive smart spaces

Gilman, Ekaterina; Davidyuk, Oleg; Su, Xiang; Riekki, Jukka

doi:10.3233/ais-120189

Cited by 23 publications

(10 citation statements)

References 61 publications

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“…Smart space interaction has received significant attention from the research community [39,72]. Shared spaces possess certain challenges in adapting the environment, since the preferences of multiple users should be respected.…”

Section: Space Interactionmentioning

confidence: 99%

Internet of Things for Smart Spaces: A University Campus Case Study

Gilman

Tamminen

Yasmin

et al. 2020

Sensors

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Advances in technology and data analysis provide rich opportunities for developing intelligent environments assisting their inhabitants, so-called smart environments or smart spaces. Enhanced with technology, sensors, user interfaces, and various applications, such smart spaces are capable of recognizing users and situations they are in, react accordingly, e.g., by providing certain services or changes to the environment itself. Therefore, smart space solutions are gradually coming to different application domains, each with corresponding specific characteristics. In this article, we discuss our experiences and explore the challenges of a long-term real-world Internet of Things (IoT) deployment at a University campus. We demonstrate the technical implementation and data quality issues. We conduct several studies, from data analysis to interaction with space, utilizing the developed infrastructure, and we also share our actions to open the data for education purposes and discuss their outcomes. With this article, we aim to share our experience and provide real-world lessons learned when building an open, multipurpose, publicly used smart space at a University campus.

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Section: Space Interactionmentioning

confidence: 99%

Internet of Things for Smart Spaces: A University Campus Case Study

Gilman

Tamminen

Yasmin

et al. 2020

Sensors

Self Cite

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“…Two exceptions are, 25,26 and they both exploited data association and applied hidden Markov models to infer activities. Gilman et al 27 also argued that smart spaces should be able not only to adapt their behavior according to the actions of humans and other participants, but also to initiate interactions when necessary. Therefore, these concerns motivated us to give further consideration to interpersonal interactions.…”

Section: Related Workmentioning

confidence: 99%

Interaction-Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

Chiang

2014

Int. J. Intell. Syst.

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Activity recognition (AR) is a key enabler for a context-aware smart home since knowing what the residents' current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real-life scenario, multiple-resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction-feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context-aware services for a multiresident smart home. C 2014 Wiley Periodicals, Inc.

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“…In several cases, content organized in narratives is also provided. In these interactive-and contentintensive systems, the user plays the role of a further agent whose behavior needs to be considered in the orchestration governing the system [31]. This technological landscape is the key for enabling Smart Interactive Experiences (SIEs-pronounced "see-ehs"), i.e., immersive experiences created by orchestrating different devices so that the resulting smart environments are completely automated to fluidly react to the usage situations induced by the situational needs of the final users [6].…”

Section: Introductionmentioning

confidence: 99%

User-defined semantics for the design of IoT systems enabling smart interactive experiences

Ardito

Desolda

Lanzilotti

et al. 2020

Pers Ubiquit Comput

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Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision of interactive and immersive experiences that actively involve the users. IoT technology thus becomes the key for Smart Interactive Experiences (SIEs), i.e., immersive automated experiences created by orchestrating different devices to enable smart environments to fluidly react to the final users’ behavior. There are domains, e.g., cultural heritage, where these systems and the SIEs can support and provide several benefits. However, experts of such domains, while intrigued by the opportunity to induce SIEs, are facing tough challenges in their everyday work activities when they are required to automate and orchestrate IoT devices without the necessary coding skills. This paper presents a design approach that tries to overcome these difficulties thanks to the adoption of ontologies for defining Event-Condition-Action rules. More specifically, the approach enables domain experts to identify and specify properties of IoT devices through a user-defined semantics that, being closer to the domain experts’ background, facilitates them in automating the IoT devices behavior. We also present a study comparing three different interaction paradigms conceived to support the specification of user-defined semantics through a “transparent” use of ontologies. Based on the results of this study, we work out some lessons learned on how the proposed paradigms help domain experts express their semantics, which in turn facilitates the creation of interactive applications enabling SIEs.

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Towards interactive smart spaces

Cited by 23 publications

References 61 publications

Internet of Things for Smart Spaces: A University Campus Case Study

Internet of Things for Smart Spaces: A University Campus Case Study

Interaction-Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

User-defined semantics for the design of IoT systems enabling smart interactive experiences

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