AIMC Topic: Movement

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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Sensors (Basel, Switzerland)
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite t...

Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

IEEE transactions on neural networks and learning systems
Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However,...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...

Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.

Neural networks : the official journal of the International Neural Network Society
Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-comp...

Machine learning-based analysis of defensive strategies in basketball using player movement data.

Scientific reports
The analysis of basketball strategies has traditionally relied on manual observation and limited data. As tracking technology progresses, there is potential for applying Artificial Intelligence specifically to strategy, delivering insights into defen...

Review of sEMG for Exoskeleton Robots: Motion Intention Recognition Techniques and Applications.

Sensors (Basel, Switzerland)
The global aging trend is becoming increasingly severe, and the demand for life assistance and medical rehabilitation for frail and disabled elderly people is growing. As the best solution for assisting limb movement, guiding limb rehabilitation, and...

Automatic video analysis of countermovement jump performance using a single uncalibrated camera.

Journal of biomechanics
The countermovement jump (CMJ) assessment is widely employed for monitoring sports performance, traditionally relying on heavy and expensive force plates to extract performance variables like jump height and peak force. Inertial measurement unit (IMU...

Learning-based 3D human kinematics estimation using behavioral constraints from activity classification.

Nature communications
Inertial measurement units offer a cost-effective, portable alternative to lab-based motion capture systems. However, measuring joint angles and movement trajectories with inertial measurement units is challenging due to signal drift errors caused by...

Tidal Volume Monitoring via Surface Motions of the Upper Body-A Pilot Study of an Artificial Intelligence Approach.

Sensors (Basel, Switzerland)
The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology throug...

AI-based human whole-body posture-prediction in continuous load reaching/leaving activities.

Journal of biomechanics
Determining worker's body posture during load handling activities is the first step toward assessing and managing occupational risk of musculoskeletal injuries. Traditional approaches for the measurement of body posture are impractical in real work s...