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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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DynAMoS: The Dynamic Affective Movie Clip Database for Subjectivity Analysis.

Jeffrey M Girard1, Yanmei Tie2, Einat Liebenthal3

  • 1Department of Psychology, University of Kansas, Lawrence, KS, USA.

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|January 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new video database capturing dynamic emotion ratings from many participants, embracing subjective emotional experiences for Affective Computing research. The database supports emotion elicitation, algorithm training, and subjectivity studies.

Keywords:
affective computingcontent analysisdatabaseemotion elicitationmovie clipsmultimodalsubjectivity

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Area of Science:

  • Affective Computing
  • Computational Neuroscience
  • Psychology

Background:

  • Previous Affective Computing research often relied on averaged emotion labels, overlooking individual subjective experiences.
  • A single "ground truth" label for affective video content limits understanding of nuanced emotional responses.

Purpose of the Study:

  • To introduce a novel video database with dynamic, holistic emotion ratings from a large participant pool.
  • To shift the paradigm in Affective Computing by embracing the subjectivity of emotional experiences.
  • To provide a resource for interdisciplinary research in emotion elicitation, algorithm development, and subjectivity analysis.

Main Methods:

  • Collected dynamic and holistic emotion ratings from 83 participants viewing 22 affective movie clips.
  • Provided the full distribution of emotion ratings (average 76.7 raters/video) instead of a single averaged label.
  • Validated the database for interdisciplinary use cases.

Main Results:

  • A comprehensive video database with rich, subjective emotion data was created.
  • The database captures the inherent subjectivity of emotional responses to media.
  • Demonstrated utility for dynamic emotion recognition, personalized algorithms, and understanding emotional ambiguity.

Conclusions:

  • Embracing subjective emotion data represents a paradigm shift for Affective Computing.
  • The database facilitates new research directions in emotion elicitation, algorithm development, and the study of emotional subjectivity.
  • The freely available database (https://dynamos.mgb.org) promotes noncommercial research.