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Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
Örebro University, Sweden.
Örebro University, Sweden.
Örebro University, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science. (Connected Intelligence)ORCID iD: 0000-0003-3139-2564
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2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods.While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning.It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 3, article id 879
Keywords [en]
smart home data sets; data collection software; prototype installation
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-43882DOI: 10.3390/s20030879Scopus ID: 2-s2.0-85079189175OAI: oai:DiVA.org:ri-43882DiVA, id: diva2:1392847
Funder
Knowledge FoundationAvailable from: 2020-02-13 Created: 2020-02-13 Last updated: 2023-05-26Bibliographically approved

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Tsiftes, Nicolas

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