A review of the key technologies for sEMG-based human-robot interaction systems
K Li, J Zhang, L Wang, M Zhang, J Li, S Bao - … Signal Processing and …, 2020 - Elsevier
As physiological signals that are closely related to human motion, surface electromyography
(sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some …
(sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some …
[HTML][HTML] Soft electronics for health monitoring assisted by machine learning
Due to the development of the novel materials, the past two decades have witnessed the
rapid advances of soft electronics. The soft electronics have huge potential in the physical …
rapid advances of soft electronics. The soft electronics have huge potential in the physical …
Artificial intelligence for the metaverse: A survey
Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …
technologies have been created to bring users breathtaking experiences with more virtual …
BiLSTM deep neural network model for imbalanced medical data of IoT systems
M Woźniak, M Wieczorek, J Siłka - Future Generation Computer Systems, 2023 - Elsevier
Health informatics is one of the most developed field in recent time. Computational
Intelligence is among the most influential factors that may help to improve patient oriented …
Intelligence is among the most influential factors that may help to improve patient oriented …
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain–computer interface
L Chen, P Chen, S Zhao, Z Luo, W Chen… - Journal of Neural …, 2021 - iopscience.iop.org
Objective. Brain-controlled robotic arms have shown broad application prospects with the
development of robotics, science and information decoding. However, disadvantages, such …
development of robotics, science and information decoding. However, disadvantages, such …
[HTML][HTML] Deep learning-based stroke disease prediction system using real-time bio signals
The emergence of an aging society is inevitable due to the continued increases in life
expectancy and decreases in birth rate. These social changes require new smart healthcare …
expectancy and decreases in birth rate. These social changes require new smart healthcare …
AI-based stroke disease prediction system using ECG and PPG bio-signals
Since stroke disease often causes death or serious disability, active primary prevention and
early detection of prognostic symptoms are very important. Stroke diseases can be divided …
early detection of prognostic symptoms are very important. Stroke diseases can be divided …
Emerging trends in BCI-robotics for motor control and rehabilitation
Neuroengineering research over the last two decades has demonstrated promising
evidence on the use of brain-computer interface (BCI) to enhance functional recovery and …
evidence on the use of brain-computer interface (BCI) to enhance functional recovery and …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …