Cortical network modeling for inverse kinematic computation of an anthropomorphic finger

Neurophysiological Correlates of Gait Retraining With Real-Time Visual and Auditory Feedback

Most people acquire motor skills through feedback-based training. How the human brain processes sensory feedbacks during training, especially in a gait training, remain largely unclear. The purpose of this paper is to explore how humans adopt a new gait pattern to reduce impacts during walking-with the aid of visual and audio feedbacks. This paper demonstrates the features of underlying brain activity in incorporating the visual or auditory cues to acquire a new gait pattern. Electroencephalography (EEG) and peak positive acceleration (PPA) of the heel were collected from 23 participants during walking on a treadmill with no feedback, with visual feedback, or with audio feedback. The feedbacks were presented after each foot strike, where a sub-threshold PPA triggered a positive feedback (green/low-pitched), and a suprathreshold PPA triggered a negative feedback (red/high-pitched). The participants were instructed to voluntarily control their gait, so that low PPA could be achieved. This control was perturbed in some sessions by an additional cognitive task, and the influence of such distraction was also explored. The PPA was significantly lower in the sessions with visual or audio feedback than in sessions without feedback, showing an immediate improvement in gait pattern, when the feedback was provided. Different feedbacks modulated neural activities at different locations and/or levels during training. Alpha event-related synchronization (ERS) was particularly increased during the encoding of auditory feedback or the introduction of a distracting task. In the meantime, prominent frontal and posterior theta ERS were coupled with negative feedback, and strong beta event-related desynchronization (ERD) was observed only in sessions with feedbacks. Our results indicate that feedback effectively enhances motor planning when acquiring a new gait.

Cortex inspired model for inverse kinematics computation for a humanoid robotic finger

In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multi-jointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands.