最小リソースでリアルタイム遷移検出を高性能化
Maximizing Performance with Minimal Resources for Real-Time Transition Detection
機械学習で活動ごとの閾値を学習し、歩行・階段・座位などの遷移をリアルタイム検出する手法を提案。処理時間を最大11倍改善し、骨盤装具実験で有効性を検証した。
著者: Zeynep Ozge Orhan, Andrea Dal Prete, Anastasia Bolotnikova, Marta Gandolla, Auke Ijspeert, Mohamed Bouri
分類: cs.RO
原文アブストラクト
Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We here present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.
