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A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolator. The sEMG detection circuit employs a stimulus isolate switch (SIS), a blanking (BLK) and non-linear feed-back (NFB) circuit to remove the artifacts and to increase the recording time. In the SIS, the connection between stimulator and stimulation electrodes, along with the stimulation electrodes and the ground are controlled by an opto-isolator, and the connection of instrument amplifier and the recording electrodes are controlled by CMOS-based switches. The mode switches of the BLK and the NFB circuit also employs CMOS-based switches. By an accurate timing adjustment, the voluntary EMG can be recorded during electrical stimulation. Two 6 able-bodied experiments have been performed to test the three anti-artifact sEMG detector: BLK, BLK&SIS, BLK&SIS&NFB. The results indicate that the BLK&SIS&NFB proposed in this work effectively removes stimulus artifacts and M-waves, and has a longer recording time compared with BLK and BLK&SIS circuits.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176373 | DOI Listing |
J Neuroeng Rehabil
November 2024
The National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Sensors (Basel)
February 2023
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy.
Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep learning (DL) models for this task on memory-constrained and battery-operated edge devices, such as wearables, requires a careful optimization process, both at design time, with an appropriate tuning of the DL models' architectures, and at execution time, where the execution of large and computationally complex models should be avoided unless strictly needed. In this work, we pursue both optimization targets, proposing a novel gesture recognition system that improves upon the state-of-the-art models both in terms of accuracy and efficiency.
View Article and Find Full Text PDFJ Electromyogr Kinesiol
June 2022
Department of Information Engineering, University of Padua, Padua, Italy; Department of Medicine, University of Padua, Padua, Italy. Electronic address:
Gait disorders are one of the cardinal features of Parkinson's Disease (PD) and might be affected by a modified pattern of motor unit activation. This work explores how PD affects the lower limb muscle control and how muscle activity contributes to gait impairment. Using clinical gait analysis data, the onset and the offset of the surface electromyographic (sEMG) signal of four lower limb muscles were determined in 18 people with PD and compared with 10 heathy controls.
View Article and Find Full Text PDFPLoS One
February 2022
School of Electronic and Information Engineering, Sanjiang University, Nanjing, Jiangsu, China.
Hand gesture recognition tasks based on surface electromyography (sEMG) are vital in human-computer interaction, speech detection, robot control, and rehabilitation applications. However, existing models, whether traditional machine learnings (ML) or other state-of-the-arts, are limited in the number of movements. Targeting a large number of gesture classes, more data features such as temporal information should be persisted as much as possible.
View Article and Find Full Text PDFJ Neuroeng Rehabil
October 2021
Department of Electronics and Telecommunications, Politecnico Di Torino, 10129, Turin, Italy.
Background: The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.
View Article and Find Full Text PDF