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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Steps in the Modeling Process01:14

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Development of a yoga posture coaching system using an interactive display based on transfer learning.

Chhaihuoy Long1, Eunhye Jo2, Yunyoung Nam3

  • 1Department of ICT Convergence, Soonchunhyang University, Asan, 31538 South Korea.

The Journal of Supercomputing
|September 27, 2021
PubMed
Summary

This study developed an AI yoga coach using transfer learning to prevent injuries from incorrect poses. The system accurately recognizes yoga postures in real-time, guiding users to proper form.

Keywords:
Posture classificationReal-time instruction feedbackSelf-coaching systemTransfer learningYoga

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Area of Science:

  • Computer Science
  • Health & Fitness Technology
  • Artificial Intelligence

Background:

  • Yoga offers significant physical and mental health benefits.
  • Incorrect yoga postures can lead to injuries like muscle sprains and pain.
  • There is a need for effective tools to guide yoga practitioners and prevent harm.

Purpose of the Study:

  • To develop an interactive yoga posture coaching system utilizing transfer learning.
  • To accurately recognize and provide real-time feedback on yoga postures.
  • To enhance user safety and improve yoga practice through technology.

Main Methods:

  • Collected RGB camera data of 14 yoga postures from 8 participants performing each pose 10 times.
  • Applied data augmentation techniques to enhance training datasets and prevent overfitting.
  • Evaluated six transfer learning models (TL-VGG16-DA, TL-VGG19-DA, TL-MobileNet-DA, TL-MobileNetV2-DA, TL-InceptionV3-DA, TL-DenseNet201-DA) for posture classification.

Main Results:

  • The TL-MobileNet-DA model demonstrated superior performance.
  • Achieved an overall accuracy of 98.43%, sensitivity of 98.30%, and specificity of 99.88%.
  • The Matthews correlation coefficient was 0.9831, indicating high classification accuracy.

Conclusions:

  • The developed yoga posture coaching system effectively recognizes user movements in real-time.
  • The system provides guidance to help users avoid incorrect postures and potential injuries.
  • Transfer learning, specifically the TL-MobileNet-DA model, is highly effective for AI-powered yoga coaching.