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A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease
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The KIT Motion-Language Dataset.

Matthias Plappert1, Christian Mandery1, Tamim Asfour1

  • 1High Performance Humanoid Technologies (H2T), Institute for Anthropomatics and Robotics (IAR), Department of Informatics, Karlsruhe Institute of Technology (KIT) , Karlsruhe, Germany .

Big Data
|December 20, 2016
PubMed
Summary
This summary is machine-generated.

Researchers created the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset to link human motion and natural language. This large, open dataset supports developing and evaluating human activity and robot activity generation systems.

Keywords:
crowd sourcingdata sethuman motionmachine learningmotion capturenatural language

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Area of Science:

  • Computer Vision and Robotics
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Linking human motion and natural language is crucial for semantic activity representation and natural language-driven robot control.
  • Existing research lacks standardized, openly available datasets for developing and evaluating such systems.

Purpose of the Study:

  • To introduce the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, a large, open, and extensible resource.
  • To provide a unified representation for motion data from diverse sources, simplifying data integration.
  • To establish a robust methodology for natural language annotation of human motion data.

Main Methods:

  • Aggregated motion capture data from multiple sources into a unified, system-independent representation.
  • Utilized a crowd-sourcing approach with a dedicated web-based tool (Motion Annotation Tool) for natural language annotation.
  • Implemented gamification strategies to maintain annotator motivation and a perplexity-based selection method to refine data quality and coverage.

Main Results:

  • The KIT Motion-Language Dataset contains 3911 motions totaling 11.23 hours, with 6278 natural language annotations comprising 52,903 words (as of October 10, 2016).
  • The perplexity-based selection method effectively addressed under-representation and erroneous annotations, ensuring a systematic annotation process.
  • The unified data representation facilitates easier use of data from various motion capture origins.

Conclusions:

  • The KIT Motion-Language Dataset is a valuable, comprehensive resource for advancing research in linking human motion and natural language.
  • The dataset and annotation methodology enable more transparent, comparable, and reproducible research in human activity and robot control.
  • This work addresses the critical need for standardized data in this interdisciplinary research area.