ウェアラブルセンサからの動作認識と全身運動・力学の同時予測
Simultaneous Action Recognition and Human Whole-Body Motion and Dynamics Prediction from Wearable Sensors
ウェアラブルセンサのデータから、人間の動作認識と全身の運動・力学をリアルタイムで同時に予測する手法を提案。専門家混合モデルをRNNで実装し、歩行や回転動作で66自由度の予測を実証した。
著者: Kourosh Darvish, Serena Ivaldi, Daniele Pucci
分類: cs.RO
原文アブストラクト
This paper presents a novel approach to solve simultaneously the problems of human activity recognition and whole-body motion and dynamics prediction for real-time applications. Starting from the dynamics of human motion and motor system theory, the notion of mixture of experts from deep learning has been extended to address this problem. In the proposed approach, experts are modelled as a sequence-to-sequence recurrent neural networks (RNN) architecture. Experiments show the results of 66-DoF real-world human motion prediction and action recognition during different tasks like walking and rotating. The code associated with this paper is available at: \url{github.com/ami-iit/paper_darvish_2022_humanoids_action-kindyn-predicition}
