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Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes.

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  • 1Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70182 Örebro, Sweden.

Sensors (Basel, Switzerland)
|February 12, 2020
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Summary
This summary is machine-generated.

Researchers developed the E-care@home system for smart home activity recognition. This system provides open datasets and software for benchmarking AI algorithms in real-world settings.

Keywords:
data collection softwareprototype installationsmart home data sets

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Smart home and activity recognition research is rapidly expanding.
  • Need for standardized benchmarks and open datasets for method comparison.
  • Existing synthetic data has limitations; real-world data is crucial.

Purpose of the Study:

  • Introduce the E-care@home system for data collection and AI reasoning.
  • Present open datasets for activity recognition in daily living.
  • Provide a benchmark system for smart home research.

Main Methods:

  • Developed E-care@home: a modular software system for data collection, labeling, and AI reasoning.
  • Integrated heterogeneous sensors with AI modules for tasks like activity recognition and person counting.
  • Collected and annotated open datasets in a real home environment.

Main Results:

  • The E-care@home system facilitates data collection, labeling, and AI-driven reasoning.
  • A series of open datasets for recognizing activities of daily living are now available.
  • Detailed technical infrastructure and physical environment for data collection are described.

Conclusions:

  • The E-care@home system and its associated datasets offer valuable resources for the research community.
  • Enables benchmarking of activity recognition algorithms using real-world smart home data.
  • Promotes advancements in AI for smart home applications and daily living assistance.