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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

484
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
484
Observational Learning01:12

Observational Learning

285
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...
285
Introduction to Learning01:18

Introduction to Learning

519
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
519

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An Immersive Human-Robot Interactive Game Framework Based on Deep Learning for Children's Concentration Training.

Li Liu1, Yangguang Liu2, Xiao-Zhi Gao3

  • 1College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China.

Healthcare (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered human-robot interactive game for children's concentration training, overcoming teacher shortages and improving focus. The system achieved high accuracy, showing promise for effective attention development.

Keywords:
concentration trainingdeep learninggesture recognitionhuman–robot interaction

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Developmental Psychology

Background:

  • Traditional concentration training methods face challenges like teacher shortages and low engagement.
  • Developing effective and accessible tools for children's attention training is crucial.

Purpose of the Study:

  • To propose and evaluate an immersive human-robot interactive (HRI) game framework using deep learning for children's concentration training.
  • To address limitations in current attention training programs.

Main Methods:

  • Developed a gesture recognition model using the YOLOv5 deep learning algorithm with 10,000 child gesture images, achieving 98.7% accuracy.
  • Implemented an HRI game framework with modules for video acquisition, image recognition, deep learning, and feedback.
  • Recruited 120 children (9-12 years) with attention deficits for HRI game experiments, collecting 8640 data samples.

Main Results:

  • The YOLOv5 gesture recognition model demonstrated high accuracy (98.7%).
  • Analysis of 8640 data samples revealed girls exhibited superior visual short-term memory and faster response times compared to boys.
  • The HRI games proved effective, convenient, and flexible for concentration training.

Conclusions:

  • The proposed deep learning-based HRI game framework is a viable and effective tool for enhancing children's concentration.
  • The findings suggest potential gender-based differences in cognitive aspects relevant to attention training.
  • HRI games offer a promising, engaging, and accessible approach to pediatric attention development.