卓球ポーズによる運動分析と,人工知能に基づく参加動機と選手の満足度の分析 YOLOv8
Kaihao Yu1, Shamsulariffin Bin Samsudin2, Mohd Aswad Ramlan3
1Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
Scientific reports
|February 15, 2026
まとめ
この研究では,テーブルテニスのポーズ推定のための軽量AIモデルであるLAFPoseを導入し,アスリートのモチベーションとトレーニングフィードバックを改善します. このシステムは,リアルタイムの運動分析を通じて,パフォーマンスの分析とアスリートのエンゲージメントを向上させます.
科学分野:
- スポーツ科学 スポーツ科学
- コンピュータ・ビジョン コンピュータ・ビジョン
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- ポーズベースの運動分析は,卓球のテクニカル評価とフィードバックに不可欠です.
- 人工知能 (AI) とポーズ推定は,リアルタイム運動分析のための高度な方法を提供します.
研究 の 目的:
- 卓球のための軽量で効率的なポーズ推定ネットワークを開発する.
- スポーツ選手のモチベーションとトレーニング経験に対するAI駆動の運動分析の影響を評価する.
主な方法:
- YOLOv8m-Poseに基づいた軽量注意力強化融合ポーズ推定ネットワーク (LAFPose) を提案しました.
- 組み込みのMobileNetV3,CBAM,アダプティブキーポイント強化,およびネットワーク最適化のためのCARAFEモジュール.
- UCF101データセットの実験と,3つのグループで120人のアスリートを対象とした経験的研究を行いました.
主要な成果:
- LAFPoseは33.2MBのモデルサイズと46GFLOPSで86.8%の精度を達成しました.
- リアルAI介入グループは,やる気 (18.45から20.75) と満足度 (3.62から4.21) のスコア (p <0.001) で有意な増加を示しました.
- AI主導のフィードバックは,アスリートのエンゲージメントを高めるのに大きな効果サイズ (Cohen's d) を示しました.
結論:
- LAFPoseは,ポーズ推定のための計算効率と精度の優れたバランスを提供します.
- AIが駆動する運動分析とリアルタイムのフィードバックは,アスリートのモチベーションとトレーニング体験を大幅に高めます.
- この発見は,インテリジェントなスポーツトレーニングシステムとスポーツ心理学に実用的な意味を持ちます.
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