AIMC Topic: Psychomotor Performance

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A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping.

IEEE transactions on haptics
Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically linked master-slave manipulators st...

Hand Extension Robot Orthosis (HERO) Glove: Development and Testing With Stroke Survivors With Severe Hand Impairment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The hand extension robot orthosis (HERO) glove was iteratively designed with occupational therapists and stroke survivors to enable stroke survivors with severe hand impairment to grasp and stabilize everyday objects, while being portable, lightweigh...

Tutorial and simulations with ADAM: an adaptation and anticipation model of sensorimotor synchronization.

Biological cybernetics
Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronizat...

Improving Reliability of Myocontrol Using Formal Verification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally, and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, int...

Deep learning for electroencephalogram (EEG) classification tasks: a review.

Journal of neural engineering
OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools ...

A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation.

IEEE transactions on neural networks and learning systems
Previous studies on robotic rehabilitation have shown that subjects' active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the reh...

The feasibility of using robotic technology to quantify sensory, motor, and cognitive impairments associated with ALS.

Amyotrophic lateral sclerosis & frontotemporal degeneration
OBJECTIVE: We used the KINARM robot to quantify impairments in cognitive and upper-limb sensorimotor performance in a cohort of people with amyotrophic lateral sclerosis (ALS). We sought to study the feasibility of using this technology for ALS resea...

An EOG-based wheelchair robotic arm system for assisting patients with severe spinal cord injuries.

Journal of neural engineering
OBJECTIVE: In this study, we combine a wheelchair and an intelligent robotic arm based on an electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control ...

Sensorimotor Robotic Measures of tDCS- and HD-tDCS-Enhanced Motor Learning in Children.

Neural plasticity
Transcranial direct-current stimulation (tDCS) enhances motor learning in adults. We have demonstrated that anodal tDCS and high-definition (HD) tDCS of the motor cortex can enhance motor skill acquisition in children, but behavioral mechanisms remai...

Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions ...