軽量ウェアラブルIMUヒューマンポーズ推定のための人間中心設計ベースの軽量ウェアラブルIMUヒューマンポーズ推定
Lidong Wang1, Juanjuan Liu1, Jingxuan Xue1
1School of Design, Sichuan Fine Arts Institute, Chongqing, 401331, China.
Scientific reports
|March 1, 2026
まとめ
本研究では、知識蒸留と構造的再パラメータ化を用いた効率的なウェアラブルヒューマンポーズ推定フレームワークを紹介します。このモデルは、低電力デバイスでのリアルタイムアプリケーションに理想的な、サブミリ秒の遅延で高い精度を達成します。
科学分野:
- コンピュータビジョン; 機械学習; ウェアラブルテクノロジー
背景:
- ヒューマンポーズ推定は、ヒューマンコンピュータインタラクションにおいて重要です。; 既存の方法は、多くの場合、かなりの計算リソースを必要とし、オンデバイス展開を制限します。; ウェアラブル慣性計測ユニット（IMU）ベースのシステムは、プライバシーを保護する代替手段を提供します。
研究 の 目的:
- ウェアラブルデバイスへのオンデバイス展開のための、非常に効率的なヒューマンポーズ推定フレームワークを開発すること。; リアルタイムアプリケーションのために、高精度と低遅延のバランスを達成すること。; モデル最適化のために、知識蒸留と構造的再パラメータ化を活用すること。
主な方法:
- Transformerベースの教師モデルが、時空間表現を学習します。; 無結合ベースの学生モデルが、入力適応型演算子を利用します。; 構造的再パラメータ化は、効率的な推論のためにトレーニンググラフを収縮させます。; 知識蒸留は、教師から学生モデルへの学習を転送します。
主要な成果:
- 提案されたフレームワークは、最先端に近い精度（DIP-IMUで81 mm MPJPE、IMUPoserで94 mm）を達成します。; サブミリ秒の遅延（DIP-IMUで0.012 ms、IMUPoserで0.011 ms）を実現します。; Transformerベースラインと比較して大幅な高速化（1〜2桁）を示します。; モデルは、堅牢性と被験者間汎化能力を示します。
結論:
- 開発されたフレームワークは、ハードウェアに優しく、低電力ウェアラブルに適しています。; エッジデバイスでの効率的でリアルタイムなヒューマンポーズ推定を可能にします。; このアプローチは、トレーニングの表現力と推論の効率性を効果的に分離します。
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