9 accepted papers
Enhancing brain activation efficiency is crucial in developing brain computer interface (BCI) paradigm for cognitive rehabilitation. However, the existing BCI paradigms mostly achieved limited sensory-activation without sufficient feedback of mind and body, significantly limiting the user engagement…
Efficient brain functional training with rehabilitation robots has been an important and challenging topic in the human-machine interaction (HMI) field. Adjusting the interaction and gaming behaviors between human and machine to effectively activate the brain’s functional behavior is still a substan…
Accurately assessing brain activity to modulate training parameters online is crucial for improving the human-robot cognitive interaction (HRCI) performance in closed-loop brain training. The major challenge for this technique lies in how to accurately model and characterize the intrinsic behavior o…
Focus of attention is one of the most influential factors facilitating motor training performance. Most of robotic training methods have not well solved the negative effect of divided-attention on motor execution performance, resulting in limited rehabilitation efficiency for motor-cognitive dysfunc
Brain-computer interface (BCI) is an important technology in developing the closed-loop brain training system for cognitive functional rehabilitation. Most of existing BCI paradigms have not ensured desired immersiveness of mind and body, thereby limiting participants’ engagement in training tasks.
Lower limb exoskeletons (LLEs) play a crucial role in assisting paraplegic patients with walking in outdoor environments characterized by complex terrains, including various stairs, slopes, and uneven grounds. However, most existing control methods for LLEs rely on predefined joint angles, lacking t
Lower Limb Exoskeletons are potential in the gait training for patients with gait disorders. For patients in the early rehabilitation stages with weak upper limb strength, it is challenge to keep balance by themselves only. A mobile robotic walker is helpful to maintain the walking balance, with the
This letter proposes an adaptive radial basis function neural network (RBF NN) based scheme for the dynamics learning and tracking control problems of a soft trunk robot. Specifically, a low-order approximate model describing the soft robot's dynamics is first derived with the finite element method
This letter presents a radial basis function neural network (RBF NN) based methodology to investigate the dynamics modeling and fault detection (FD) problems for soft robots. Finite element method (FEM) is first used to derive a mathematical model to describe the dynamics of a soft trunk robot. An a