AIMC Topic: Movement

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JMM-TGT: Self-supervised 3D action recognition through joint motion masking and topology-guided transformer.

PloS one
In the field of 3D skeleton action recognition, research on self-supervised learning methods has primarily focused on spatio-temporal feature modeling. However, these methods rely heavily on modeling single motion features, which limits their ability...

Evaluating corticokinematic coherence using electroencephalography and human pose estimation.

Biomedical physics & engineering express
While peripheral mechanisms of proprioception are well understood, the cortical processing of its feedback during dynamic and complex movements remains less clear. Corticokinematic coherence (CKC), which quantifies the coupling between limb movements...

Impact of the breathing motion prediction horizon on the performance of bidirectional classical recurrent neural and temporal Kolmogorov-Arnold networks.

Physics in medicine and biology
For surface-based breathing motion prediction, which is essential to overcome inherent system latencies of active motion management strategies in radiotherapy, long short-term memory (LSTM) networks and related networks-bidirectional LSTMs (BiLSTMs),...

Robotic manipulation of human bipedalism reveals overlapping internal representations of space and time.

Science robotics
Effective control of bipedal postures relies on sensory inputs from the past, which encode dynamic changes in the spatial properties of our movement over time. To uncover how the spatial and temporal properties of an upright posture interact in the p...

Wearable sensing for badminton stroke recognition with one-dimensional convolutional neural network.

Scientific reports
Motivated by the need to improve the performance of badminton players, various motion monitoring systems have been developed to assist coaches in badminton technique instruction. While traditional video or optical methods are limited to fixed scenari...

A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.

PloS one
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...

Deep learning-driven contactless ECG in MRI via beat pilot tone for motion-resolved image reconstruction and heart rate monitoring.

Physics in medicine and biology
Electrocardiogram (ECG) is crucial for synchronizing cardiovascular magnetic resonance imaging (CMRI) acquisition with the cardiac cycle and for continuous heart rate monitoring during prolonged scans. However, conventional electrode-based ECG system...

Efficient elastic tissue motions indicate general motor skill.

Scientific reports
Insights into the general nature of motor skill could fundamentally change how we develop movement abilities, with implications for musculoskeletal well-being and injury. Here, we sought to identify indicators of general motor skill-those shared by e...

An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.

International journal of neural systems
Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming a...