Model-Agnostic Personalized Knowledge Adaptation for Soft Exoskeleton Robot

Personalized control strategies can improve the overall performance of exoskeleton robots by providing adaptive assistance to different individuals. However, current personalized strategies require accurate models or large amounts of training data, which are not suitable for soft exoskeletons due to human-robot interaction uncertainties and low data acquisition efficiency. This paper presents a framework of Model-Agnostic Personalized Knowledge Adaptation (MPKA) consisting of two modules of Soft-to-Rigid Kinematics Model (SRKM) and Data-Driven Personalized Adaptation (DDPA) to generate a personalized strategy. Specifically, the first SRKM module is used to simplify and convert uncertain and time-varying HRI model to a certain and fixed model. The second DDPA module is used to accelerate model adaptation to new individuals using meta-learning with less data, based on the learned knowledge from the previous subjects. The experiments were conducted on three different sized platforms, four healthy and one post-stroke subjects, respectively. The results demonstrate that the MPKA can generate personalized models for new individuals more rapidly with less data than a pre-trained neural network, and the personalized models can obtain better control accuracy by over 62% than mechanism-based mathematical model. The proposed method has the potential to provide personalized rehabilitation strategies, which can improve effectiveness of rehabilitation.