オンライン学習制御による個別化リハビリテーションロボティクス
Personalized Rehabilitation Robotics based on Online Learning Control
ガウス過程に基づくオンライン学習で、実行時に各患者へアシスト力を自動で個別適応させるリハビリ制御手法を提案し、ユーザー実験で安全性と個別化を検証した。
著者: Samuel Tesfazgi, Armin Lederer, Johannes F. Kunz, Alejandro J. Ordóñez-Conejo, Sandra Hirche
分類: cs.LG, cs.RO
原文アブストラクト
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works. However, their practical utility is dependent on the deployment of appropriate control algorithms, which adapt the level of task-assistance according to each individual patient's need. Generally, the required personalization is achieved through manual tuning by clinicians, which is cumbersome and error-prone. In this work we propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user. To this end, we deploy Gaussian process-based online learning with previously unseen prediction and update rates. Finally, we evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
