Learning Activity Models for Multiple Agents in a Smart Space

Crandall, Aaron S.; Cook, Diane J.

doi:10.1007/978-0-387-93808-0_28

Cited by 7 publications

(2 citation statements)

References 22 publications

(23 reference statements)

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“…The goal of this project is to treat environments as intelligent agents, where the status of the residents and their physical surroundings are perceived using sensors; on the basis of this information, a smart home can select and automate actions that meet the needs and preferences of its inhabitants, improving the comfort, the safety and/or the productivity. The reason for this choice relies on the major impact this project has had on the community and the availability as source code of algorithms that are often used as benchmarks [15,16,20,23,28,37].…”

Section: Truthfulness Evaluationmentioning

confidence: 99%

A model-based simulator for smart homes: Enabling reproducibility and standardization

Veneruso

Bertrand

Leotta

et al. 2023

AIS

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Scientific contributions in the area of smart environments cover different tasks of ambient intelligence including action and activity recognition, anomaly detection, and automated enactment. Algorithms solving these tasks need to be validated against sensor logs of smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators and an acquisition infrastructure. Even though several freely accessible datasets are available, each of them features a very specific set of sensors, which can limit the introduction of novel approaches that could benefit of particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires a considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. In this paper, we propose a model-based simulator capable to generate synthetic datasets that emulate the characteristics of the vast majority of real datasets while granting trustworthy evaluation results. The datasets are generated using the eXtensible Event Stream – XES international standard commonly used for representing event logs. Finally, the datasets produced by the simulator are validated against two real scenario’s logs from the literature.

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Section: Truthfulness Evaluationmentioning

confidence: 99%

A model-based simulator for smart homes: Enabling reproducibility and standardization

Veneruso

Bertrand

Leotta

et al. 2023

AIS

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“…Additionally, activity recognition itself becomes much trickier in a multi-user environment [28,33]. To approach this issue, different temporal and behavioral aspects of users can be studied, e.g., time of the day, day of the week of certain activity [34]. Another solution that is based on multi-label classification is proposed [35], allowing for detecting several activities of multiple users.…”

Section: Smart Shared Spacesmentioning

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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Combining emerging patterns with random forest for complex activity recognition in smart homes

Malazi

Davari

2017

Appl Intell

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Learning Activity Models for Multiple Agents in a Smart Space

Cited by 7 publications

References 22 publications

A model-based simulator for smart homes: Enabling reproducibility and standardization

A model-based simulator for smart homes: Enabling reproducibility and standardization

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

Combining emerging patterns with random forest for complex activity recognition in smart homes

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