Emerging wearable interfaces and algorithms for hand gesture recognition: A survey
Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …
that impede hand function can significantly affect quality of life. Wearable hand gesture …
Deep learning for EMG-based human-machine interaction: A review
D **ong, 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 …
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
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 …
EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals
Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …
classification of hand activities from surface Electromyography (sEMG) signals. However …
Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method
This paper presents an effective transfer learning (TL) strategy for the realization of surface
electromyography (sEMG)-based gesture recognition with high generalization and low …
electromyography (sEMG)-based gesture recognition with high generalization and low …
State-of-the-art human-computer-interaction in metaverse
Z Lyu - International Journal of Human–Computer Interaction, 2024 - Taylor & Francis
With the increasing popularity of the Metaverse concept, human beings have stepped to a
new height in the intelligent technology progress. This work presents a literature review of …
new height in the intelligent technology progress. This work presents a literature review of …
[HTML][HTML] Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Assessing land-use following deforestation is vital for reducing emissions from deforestation
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals
Designing efficient and labor-saving prosthetic hands requires powerful hand gesture
recognition algorithms that can achieve high accuracy with limited complexity and latency. In …
recognition algorithms that can achieve high accuracy with limited complexity and latency. In …