Personalized Rehabilitation Robotics based on Online Learning Control
This work addresses the need for automated personalization in rehabilitation robotics to reduce clinician workload and improve patient-specific therapy.
The authors tackled the problem of manual tuning in rehabilitation robotics by proposing an online learning control architecture that personalizes control force for each patient, achieving safe interaction forces in an experimental user study.
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.