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Related Concept Videos

Observational Learning01:12

Observational Learning

262
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...
262

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Perspective on "in the wild" movement analysis using machine learning.

Eva Dorschky1, Valentina Camomilla2, Jesse Davis3

  • 1Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Human Movement Science
|December 9, 2022
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Summary
This summary is machine-generated.

Wearable sensors and machine learning enable "in the wild" sports movement analysis for real-time feedback and long-term injury prevention. This paper guides setting up measurement protocols and training effective machine learning models.

Keywords:
Free-livingMachine learningMovement analysisSportsWearable sensors

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Data Science

Background:

  • Wearable sensors and machine learning (ML) advance sports movement analysis.
  • Opportunities exist for "in the wild" data collection and analysis.
  • Real-time feedback and long-term monitoring are key applications.

Purpose of the Study:

  • To provide an overview of approaches for "in the wild" sports movement analysis using wearable sensors and ML.
  • To guide the setup of measurement protocols.
  • To offer recommendations for effective ML model training and highlight application domains.

Main Methods:

  • Discussing a six-question framework for measurement protocol setup.
  • Detailing data pre-processing, feature calculation, and ML model selection/tuning.
  • Reviewing existing literature and application examples.

Main Results:

  • "In the wild" analysis enables real-time feedback for performance enhancement and technique analysis.
  • Long-term movement monitoring aids in injury prevention.
  • Effective ML model training requires careful data handling and model selection.

Conclusions:

  • Combining wearable sensing and ML offers significant potential for sports performance and injury prevention.
  • A structured approach to data collection and ML model development is crucial for successful "in the wild" analysis.
  • Future research should focus on refining protocols and exploring new applications.