Using backward adjustment with model predictive control for adaptive control of nonlinear soft artificial muscle.

Soft artificial muscles possess inherent compliance and safety features, rendering them highly suitable for applications in wearable robots and unstructured environments. However, accurately modeling the nonlinearity of soft actuators proves to be a challenging task. In this paper, we present an adaptive control method that leverages model learning and model parameter backward adjustment.

An interactive soft robotic hand-task training system with wireless task boards and daily objects on post-stroke rehabilitation.

We have developed an interactive system comprising a soft wearable robot hand and a wireless task board, facilitating the interaction between the hand and regular daily objects for task-oriented training in stroke rehabilitation. A ring-reinforced soft actuator (RSA) to accommodate different hand sizes and enable flexion and extension movements was introduced in this paper. Individually controlled finger actuators assist stroke patients during various grasping tasks.

Task-Oriented Training by a Personalized Electromyography-Driven Soft Robotic Hand in Chronic Stroke: A Randomized Controlled Trial.

Background: Intensive task-oriented training has shown promise in enhancing distal motor function among patients with chronic stroke. A personalized electromyography (EMG)-driven soft robotic hand was developed to assist task-oriented object-manipulation training effectively. To compare the effectiveness of task-oriented training using the EMG-driven soft robotic hand.