外骨格スーツのためのユーザー適応型歩行モード予測学習
User-Tailored Learning to Forecast Walking Modes for Exosuits
軽量な外骨格スーツのユーザーに合わせて、慣性センサのみから階段昇降や平地歩行などの歩行モードを推定する機械学習モジュールを提案し、オンラインでの自己ラベリングによる適応と閉ループ制御での検証を行った。
詳しい要約
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著者: Gabriele Abbate, Enrica Tricomi, Nathalie Gierden, Alessandro Giusti, Lorenzo Masia, Antonio Paolillo
分類: cs.RO
原文アブストラクト
Assistive robotic devices, like soft lower-limb exoskeletons or exosuits, are widely spreading with the promise of helping people in everyday life. To make such systems adaptive to the variety of users wearing them, it is desirable to endow exosuits with advanced perception systems. However, exosuits have little sensory equipment because they need to be light and easy to wear. This paper presents a perception module based on machine learning that aims at estimating 3 walking modes (i.e., ascending or descending stairs and walking on level ground) of users wearing an exosuit. We tackle this perception problem using only inertial data from two sensors. Our approach provides an estimate for both future and past timesteps that supports control and enables a self-labeling procedure for online model adaptation. Indeed, we show that our estimate can label data acquired online and refine the model for new users. A thorough analysis carried out on real-life datasets shows the effectiveness of our user-tailored perception module. Finally, we integrate our system with the exosuit in a closed-loop controller, validating its performance in an online single-subject experiment.
