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[HTML][HTML] EMG-centered multisensory based technologies for pattern recognition in rehabilitation: state of the art and challenges
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in
interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the …
interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the …
Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
Surface Electromyography (sEMG) has become an essential tool in various fields, including
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
[HTML][HTML] Estimation of knee movement from surface EMG using random forest with principal component analysis
Z Li, X Guan, K Zou, C Xu - Electronics, 2019 - mdpi.com
To study the relationship between surface electromyography (sEMG) and joint movement,
and to provide reliable reference information for the exoskeleton control, the sEMG and the …
and to provide reliable reference information for the exoskeleton control, the sEMG and the …
Lower limb motion intent recognition based on sensor fusion and fuzzy multitask learning
E Wang, X Chen, Y Li, Z Fu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Lower limb motion intent recognition is a crucial aspect of wearable robot control and human–
machine collaboration. Among the various sensors used for this purpose, the …
machine collaboration. Among the various sensors used for this purpose, the …
Influence of EMG-signal processing and experimental set-up on prediction of gait events by neural network
Abstract Machine-learning approaches are satisfactorily implemented for classifying and
assessing gait events from only surface electromyographic (sEMG) signals during walking …
assessing gait events from only surface electromyographic (sEMG) signals during walking …
Flexible and wearable EMG and PSD sensors enabled locomotion mode recognition for IoHT-based in-home rehabilitation
Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years,
locomotion mode recognition using wearable sensors plays a more and more important role …
locomotion mode recognition using wearable sensors plays a more and more important role …
[HTML][HTML] Recognition of gait phases with a single knee electrogoniometer: A deep learning approach
Artificial neural networks were satisfactorily implemented for assessing gait events from
different walking data. This study aims to propose a novel approach for recognizing gait …
different walking data. This study aims to propose a novel approach for recognizing gait …
Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals
Background Machine learning models were satisfactorily implemented for estimating gait
events from surface electromyographic (sEMG) signals during walking. Most of them are …
events from surface electromyographic (sEMG) signals during walking. Most of them are …
Deep learning model for predicting rhythm outcomes after radiofrequency catheter ablation in patients with atrial fibrillation
Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter
ablation (RFCA) should be decided after fully considering its prognosis. However, a robust …
ablation (RFCA) should be decided after fully considering its prognosis. However, a robust …
A real-time gait phase recognition method based on multi-information fusion
In this paper, a novel recognition method of multi-information fusion is proposed to improve
the recognition accuracy of the gait phase. Firstly, a multi-information wireless multi-channel …
the recognition accuracy of the gait phase. Firstly, a multi-information wireless multi-channel …