AIMC Topic: Movement

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A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

Physiological measurement
This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications....

Artificial neural network EMG classifier for functional hand grasp movements prediction.

The Journal of international medical research
Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-fre...

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...

Performance-based robotic assistance during rhythmic arm exercises.

Journal of neuroengineering and rehabilitation
BACKGROUND: Rhythmic and discrete upper-limb movements are two fundamental motor primitives controlled by different neural pathways, at least partially. After stroke, both primitives can be impaired. Both conventional and robot-assisted therapies mai...

Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study.

Journal of bodywork and movement therapies
This paper, presents an inertial and magnetic sensor based technological platform, intended for articular amplitude monitoring and telerehabilitation processes considering an efficient cost/technical considerations compromise. The particularities of ...

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

BMC research notes
BACKGROUND: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple ...

Modeling and analysis of bio-syncretic micro-swimmers for cardiomyocyte-based actuation.

Bioinspiration & biomimetics
Along with sensation and intelligence, actuation is one of the most important factors in the development of conventional robots. Many novel achievements have been made regarding bio-based actuators to solve the challenges of conventional actuation. H...

Robot-Aided Mapping of Wrist Proprioceptive Acuity across a 3D Workspace.

PloS one
Proprioceptive signals from peripheral mechanoreceptors form the basis for bodily perception and are known to be essential for motor control. However we still have an incomplete understanding of how proprioception differs between joints, whether it d...

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by el...

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms.

Journal of visualized experiments : JoVE
Mirror therapy has been performed as effective occupational therapy in a clinical setting for functional recovery of a hemiplegic arm after stroke. It is conducted by eliciting an illusion through use of a mirror as if the hemiplegic arm is moving in...