モーションテープと機械学習によるボクシングパンチ検出と分類

Shih-Chao Huang¹, Taylor Pierce², Yun-An Lin¹

¹Active, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Sensors (Basel, Switzerland)

|August 28, 2025

まとめ

マシン・ラーニングはボクシングのパンチをウェアラブル・モーション・テープ・センサから得られた皮膚ストレスのデータを使って精密に分類しますこの技術は,スポーツやバイオメカニクスにおけるアスリートのパフォーマンスを分析するのに役立ちます.

科学分野:

スポーツ科学
バイオメカニクス
ウェアラブルテクノロジー

背景:

ボクシングのパフォーマンス分析は,主観的な観察や複雑な機器に依存します.
ウェアラブルセンサは客観的でリアルタイムなデータ収集の解決策となる可能性があります

研究の目的:

機械学習アルゴリズムを使ってボクシングのパンチタイプを分類する
ボクシング運動中に皮膚のストレスのデータを記録するウェアラブルセンサー (モーションテープ) の有効性を評価する.

主な方法:

ボクシングのトレーニングを含むヒト参加者研究が行われました.
被験者は重い袋を打つか打たずで刺さりとハンカチを演じました
皮膚ストレインのタイムヒストリーデータは,モーションテープを使用して収集され,タイムシリーズ分類アルゴリズムで処理されました.

主要な成果:

機械学習モデルでは皮膚張りの測定に基づいて異なるパンチタイプを順調に分類しました
モーションテープシステムは,様々なパンチと条件を区別する上で有効性を示しました.

結論:

ウェアラブル・モーション・テープセンサーと機械学習が組み合わさってボクシングのパンチを効果的に分類できます
このシステムは,スポーツやバイオメカニクスにおける客観的な人間のパフォーマンスの分析のための大きな可能性を示しています.

キーワード:

開始時間ミニロケット分類する移動スポーツ指導された学習タイムシリーズ・トランスフォーマートレーニングウェアラブルセンサー

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