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Motor Activity

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EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect.

Consciousness and cognition
fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without reliance on behaviour. Because using fMRI in patients with DOC is difficult, studies increasingly employ E...

Activity Recognition for Diabetic Patients Using a Smartphone.

Journal of medical systems
Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines ...

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

Journal of computational neuroscience
Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor t...

Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study.

IEEE journal of biomedical and health informatics
The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjec...

Evaluation of Strategies for Integrated Classification of Visual-Manual and Cognitive Distractions in Driving.

Human factors
BACKGROUND: Prior studies have demonstrated unique driver behavior outcomes when visual and cognitive distraction occurs simultaneously as compared to the occurrence of one form of distraction alone. This situation implies additional complexity for t...

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

International journal of neural systems
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shap...

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.

International journal of neural systems
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification a...

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

NeuroImage
Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the succes...

Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.

Applied ergonomics
In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological a...

Making Activity Recognition Robust against Deceptive Behavior.

PloS one
Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are...