AIMC Topic: Motor Activity

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Spatially regularized machine learning for task and resting-state fMRI.

Journal of neuroscience methods
BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.

A neural network that finds a naturalistic solution for the production of muscle activity.

Nature neuroscience
It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we tr...

FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

TheScientificWorldJournal
An ontology-driven safe food consumption mobile system is considered. Over 3,000 compounds are being added to processed food, with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emuls...

Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors.

IEEE journal of biomedical and health informatics
This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from d...

A realistic bi-hemispheric model of the cerebellum uncovers the purpose of the abundant granule cells during motor control.

Frontiers in neural circuits
The cerebellar granule cells (GCs) have been proposed to perform lossless, adaptive spatio-temporal coding of incoming sensory/motor information required by downstream cerebellar circuits to support motor learning, motor coordination, and cognition. ...

A neural network-based optimal spatial filter design method for motor imagery classification.

PloS one
In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classificat...

Considering the effects of gender in child-robot interaction studies: comment on Srinivasan, et Al. (2013).

Perceptual and motor skills
Using a pretest-posttest design, Srinivasan, et al. (2013 ) found that a period of interaction between children and an Isobot humanoid robot improved performance on standardized measures of imitation, planning, and execution of motor behaviors. The a...

Bridging the gap between motor imagery and motor execution with a brain-robot interface.

NeuroImage
According to electrophysiological studies motor imagery and motor execution are associated with perturbations of brain oscillations over spatially similar cortical areas. By contrast, neuroimaging and lesion studies suggest that at least partially di...

Robust sensorimotor representation to physical interaction changes in humanoid motion learning.

IEEE transactions on neural networks and learning systems
This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply th...

Prediction of activity type in preschool children using machine learning techniques.

Journal of science and medicine in sport
OBJECTIVES: Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting ...