A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Open access dataset, toolbox and benchmark processing results of high-density surface electromyogram recordings
We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG)
Recordings (named “Hyser”), a toolbox for neural interface research, and benchmark results …
Recordings (named “Hyser”), a toolbox for neural interface research, and benchmark results …
Toward robust, adaptiveand reliable upper-limb motion estimation using machine learning and deep learning–A survey in myoelectric control
To develop multi-functionalhuman-machine interfaces that can help disabled people
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) …
Toward generalization of sEMG-based pattern recognition: A novel feature extraction for gesture recognition
Gesture recognition via surface electromyography (sEMG) has drawn significant attention in
the field of human–computer interaction. An important factor limiting the performance of …
the field of human–computer interaction. An important factor limiting the performance of …
Explainable and robust deep forests for EMG-force modeling
Machine and deep learning techniques have received increasing attentions in estimating
finger forces from high-density surface electromyography (HDsEMG), especially for neural …
finger forces from high-density surface electromyography (HDsEMG), especially for neural …
A transfer learning based cross-subject generic model for continuous estimation of finger joint angles from a new user
Continuous estimation of finger joints based on surface electromyography (sEMG) has
attracted much attention in the field of human-machine interface (HMI). A couple of deep …
attracted much attention in the field of human-machine interface (HMI). A couple of deep …
Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers
Surface electromyogram (sEMG)-based human–computer interface (HCI) is an effective tool
for detecting human movements. Because sEMG-based motion recognition usually requires …
for detecting human movements. Because sEMG-based motion recognition usually requires …
Generalized EMG-based isometric contact force estimation using a deep learning approach
EMG-based force estimation is generally done in a subject specific manner. In this paper, we
explore force estimation in a manner generalizable across individuals, where the EMG …
explore force estimation in a manner generalizable across individuals, where the EMG …
A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition
Y Liu, X Peng, Y Tan, TT Oyemakinde… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. Surface electromyography pattern recognition (sEMG-PR) is considered as a
promising control method for human-machine interaction systems. However, the …
promising control method for human-machine interaction systems. However, the …
Random channel masks for regularization of least squares-based finger EMG-force modeling to improve cross-day performance
Estimating the finger forces from surface electromyography (sEMG) is essential for diverse
applications (eg, human-machine interfacing). The performance of pre-trained sEMG-force …
applications (eg, human-machine interfacing). The performance of pre-trained sEMG-force …