肘エクソスケルトンロボットの感覚ベースの運動意図推定と安全な追跡のための異なるコントローラーの評価(LSTMネットワークベース)
Farshad Shakeriaski1, Masoud Mohammadian1
1Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia.
Sensors (Basel, Switzerland)
|January 28, 2026
まとめ
本研究では、高密度表面筋電図(HD-sEMG)とLSTMネットワークを使用して、肘エクソスケルトンのリアルタイム運動意図追跡を改善するインテリジェント制御フレームワークを開発しました。スライディングモードコントローラーは、神経リハビリテーションアプリケーションにおいて優れた精度と堅牢性を示しました。
背景:
- リハビリテーション.,.,毎年.,.,エクソスケルトン.,.
研究 の 目的:
- 正確かつ安全な.,.,高密度表面筋電図(HD-sEMG)信号から.,.,PID、インピーダンス、および.,.
主な方法:
- 12人の.,.,EMG信号.,.,LSTMネットワーク.,.,(PID、インピーダンス、スライディングモード)。
主要な成果:
- LSTMモデルは、高い精度(RMSE=0.630 Nm、R²=0.965、Pearson=0.985)を達成し、従来の.,.,スライディングモードコントローラーは、すべてのタスクと.,.,インピーダンスコントローラーは、.,.,PID制御は.,.
結論:
- 提案されたLSTM-スライディングモード.,.,この.,.,.,さらなる.,.
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