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Related Concept Videos

Force Classification01:22

Force Classification

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Related Experiment Video

Updated: Jun 13, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

A Deep Learning Approach to Automatically Classify Ice Hockey Shooting Actions Using Acceleration Signals.

Samuel Tremblay1, Philippe J Renaud1, Shawn M Robbins2,3

  • 1Department of Kinesiology & Physical Education, McGill University, Montreal, QC H2W 1S4, Canada.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately detect ice hockey stick actions using wearable sensors. Even a minimal sensor setup, like gloves, shows high accuracy for objective performance feedback.

Keywords:
human activity recognitionice hockeymachine learningshootingwearable sensors

Related Experiment Videos

Last Updated: Jun 13, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

  • Sports Science
  • Machine Learning
  • Wearable Technology

Background:

  • Objective performance evaluation in ice hockey is crucial for player development.
  • Current methods often rely on subjective coach assessments.
  • Wearable sensors and machine learning offer potential for automated activity detection.

Purpose of the Study:

  • To evaluate a deep learning model's ability to recognize ice hockey stick actions from inertial measurement unit (IMU) sensor data.
  • To compare the effectiveness of a comprehensive sensor setup versus a minimal, hands-only configuration.

Main Methods:

  • A fully connected convolutional neural network (CNN) was developed to classify seven ice hockey actions.
  • Data were collected from 43 elite players using two sensor configurations: 17 sensors (all-sensor) and 2 sensors (hands-only).
  • Standard machine learning practices including train/test splits, cross-validation, and repeated random splits were employed.

Main Results:

  • The CNN model demonstrated high classification accuracy for both sensor setups.
  • The all-sensor model achieved an average F1 score of 95.0 ± 3.0%.
  • The hands-only model achieved a comparable average F1 score of 93.5 ± 1.6%.

Conclusions:

  • Convolutional neural networks are effective for automatic ice hockey shooting action classification.
  • Minimal sensor configurations, such as integrated gloves, are feasible for practical, real-world applications.
  • This technology can provide objective performance metrics to enhance coaching and player training.