Deep learning for EMG-based human-machine interaction: A review
D Xiong, D Zhang, X Zhao… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
(HMI) applications. Determining how to decode the information inside EMG signals robustly …
Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future
W Li, P Shi, H Yu - Frontiers in neuroscience, 2021 - frontiersin.org
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …
and limits their performance in activities of daily life. The realization of natural control for …
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) …
Deep unsupervised domain adaptation with time series sensor data: A survey
Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …
used in all aspects of our social production activities. The continuous recording of physical …
Novel wearable HD-EMG sensor with shift-robust gesture recognition using deep learning
In this work, we present a hardware-software solution to improve the robustness of hand
gesture recognition to confounding factors in myoelectric control. The solution includes a …
gesture recognition to confounding factors in myoelectric control. The solution includes a …
Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics
Surface electromyography (sEMG) signals have been used for advanced prosthetics control,
hand-gesture recognition (HGR), and more recently as a novel biometric trait. For these …
hand-gesture recognition (HGR), and more recently as a novel biometric trait. For these …
Improving the robustness and adaptability of sEMG-based pattern recognition using deep domain adaptation
P Shi, X Zhang, W Li, H Yu - IEEE journal of biomedical and …, 2022 - ieeexplore.ieee.org
The pattern recognition (PR) based on surface electromyography (sEMG) could improve the
quality of daily life of amputees. However, the lack of robustness and adaptability hinders its …
quality of daily life of amputees. However, the lack of robustness and adaptability hinders its …
A framework and call to action for the future development of EMG-based input in HCI
Electromyography (EMG) has been explored as an HCI input modality following a long
history of success for prosthesis control. While EMG has the potential to address a range of …
history of success for prosthesis control. While EMG has the potential to address a range of …
Deep cross-user models reduce the training burden in myoelectric control
The effort, focus, and time to collect data and train EMG pattern recognition systems is one of
the largest barriers to their widespread adoption in commercial applications. In addition to …
the largest barriers to their widespread adoption in commercial applications. In addition to …
Deep learning for biosignal control: Insights from basic to real-time methods with recommendations
Objective. Biosignal control is an interaction modality that allows users to interact with
electronic devices by decoding the biological signals emanating from the movements or …
electronic devices by decoding the biological signals emanating from the movements or …