AIMC Topic: Electromyography
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Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 27, 2024
Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by t...
3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.
Biomaterials
May 24, 2024
The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) a...
Portable Facial Expression System Based on EMG Sensors and Machine Learning Models.
Sensors (Basel, Switzerland)
May 23, 2024
One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy ...
Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.
Sensors (Basel, Switzerland)
May 21, 2024
The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable sy...
Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.
IEEE transactions on bio-medical engineering
May 20, 2024
OBJECTIVE: Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decod...
Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.
Sensors (Basel, Switzerland)
May 13, 2024
During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (s...
Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.
Journal of neuroengineering and rehabilitation
May 9, 2024
BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adu...
Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis.
PloS one
May 7, 2024
Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding t...
Machine learning-empowered sleep staging classification using multi-modality signals.
BMC medical informatics and decision making
May 6, 2024
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...
Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.
IEEE journal of biomedical and health informatics
May 6, 2024
Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively...
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