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 …

[HTML][HTML] Soft electronics for health monitoring assisted by machine learning

Y Qiao, J Luo, T Cui, H Liu, H Tang, Y Zeng, C Liu… - Nano-Micro Letters, 2023 - Springer
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 …

Artificial intelligence for the metaverse: A survey

T Huynh-The, QV Pham, XQ Pham, TT Nguyen… - … Applications of Artificial …, 2023 - Elsevier
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 …

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 …

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

C Zhang, YK Kim, A Eskandarian - Journal of Neural Engineering, 2021 - iopscience.iop.org
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
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 …

[HTML][HTML] Deep learning-based stroke disease prediction system using real-time bio signals

YA Choi, SJ Park, JA Jun, CS Pyo, KH Cho, HS Lee… - Sensors, 2021 - mdpi.com
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 …

AI-based stroke disease prediction system using ECG and PPG bio-signals

J Yu, S Park, SH Kwon, KH Cho, H Lee - Ieee Access, 2022 - ieeexplore.ieee.org
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 …

Emerging trends in BCI-robotics for motor control and rehabilitation

N Robinson, R Mane, T Chouhan, C Guan - Current Opinion in Biomedical …, 2021 - Elsevier
Neuroengineering research over the last two decades has demonstrated promising
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

S Gong, K Xing, A Cichocki, J Li - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …