AI Medical Compendium Topic:
Movement

Clear Filters Showing 851 to 860 of 1003 articles

Natural Grasp Intention Recognition Based on Gaze in Human-Robot Interaction.

IEEE journal of biomedical and health informatics
OBJECTIVE: While neuroscience research has established a link between vision and intention, studies on gaze data features for intention recognition are absent. The majority of existing gaze-based intention recognition approaches are based on delibera...

A low-frequency acceleration sensor inspired by saccule in human vestibule.

The Review of scientific instruments
A human vestibular system is a group of devices in the inner ear that govern the balancing movement of the head, in which the saccule is responsible for sensing gravity accelerations. Imitating the sensing principle and structure of the Sensory Hair ...

Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture only the spatial information of the surface electromyography and lack prior knowledge of the system, r...

Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved in...

Spring damping based control for a novel lower limb rehabilitation robot with active flexible training planning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: During neurological rehabilitation training for patients with lower limb dysfunction, active rehabilitation training based on interactive force recognition can effectively improve participation and efficiency in rehabilitation training.

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator.

Studies in health technology and informatics
This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning...

Development of a Belt-actuated Robotic Platform for Early Rehabilitation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
In order to promote early rehabilitation, we proposed a system which provides full-body arm-leg training for patients in a bed-lying position. As the preliminary development, a platform for leg movement was investigated. An innovative system with fou...

ARMStick - An Intuitive Therapist Interface for Upper-Limb Rehabilitation Robots.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Currently, therapists struggle with interaction of rehabilitation robots due to non-intuitive interfaces. Therefore their acceptance of these robots are limited. This paper presents the development of ARMStick, a lightweight and small robotic interfa...

Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on th...

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, i...