AIMC Topic: Motor Activity
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Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits.
IEEE transactions on neural networks and learning systems
Dec 28, 2018
Upper confidence bound (UCB) is a successful multiarmed bandit for regret minimization. The covariance matrix adaptation (CMA) for Pareto UCB (CMA-PUCB) algorithm considers stochastic reward vectors with correlated objectives. We upper bound the cumu...
Sensorimotor Robotic Measures of tDCS- and HD-tDCS-Enhanced Motor Learning in Children.
Neural plasticity
Dec 18, 2018
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...
Robot-assisted gait training effectively improved lateropulsion in subacute stroke patients: a single-blinded randomized controlled trial.
European journal of physical and rehabilitation medicine
Dec 3, 2018
BACKGROUND: Some stroke patients are known to use nonparetic extremities to push toward the paretic side, a movement known as lateropulsion. Lateropulsion impairs postural balance and interferes with rehabilitation.
Toward a motor signature in autism: Studies from human-machine interaction.
L'Encephale
Nov 29, 2018
BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-communication and repetitive symptoms and stereotypies. Although not cardinal symptoms per se, motor impa...
Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.
Computational intelligence and neuroscience
Oct 18, 2018
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studie...
Activity-aware essential tremor evaluation using deep learning method based on acceleration data.
Parkinsonism & related disorders
Aug 8, 2018
BACKGROUND: Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during di...
Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 2, 2018
BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behavioral outcomes, including anxiety and depressive mood, which may compromise the rehabilitation process. Some personality features, such as self-effica...
Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 2, 2018
BACKGROUND: The objective of this study was to investigate, in subject with stroke, the exact role as prognostic factor of common inflammatory biomarkers and other markers in predicting motor and/or cognitive improvement after rehabilitation treatmen...
Treatment Outcome
Longitudinal Studies
Health Status
Inpatients
Recovery of Function
Female
Male
Time Factors
Cognition
Inflammation Mediators
Linear Models
Motor Skills
Leukocyte Count
Motor Activity
Predictive Value of Tests
Disability Evaluation
Stroke
Hematocrit
Humans
Machine Learning
Decision Support Techniques
Biomarkers
Stroke Rehabilitation
Reliability, validity and discriminant ability of the instrumental indices provided by a novel planar robotic device for upper limb rehabilitation.
Journal of neuroengineering and rehabilitation
May 16, 2018
BACKGROUND: In the last few years, there has been an increasing interest in the use of robotic devices to objectively quantify motor performance of patients after brain damage. Although these robot-derived measures can potentially add meaningful info...
A fresh look at functional link neural network for motor imagery-based brain-computer interface.
Journal of neuroscience methods
May 4, 2018
BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly app...
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