Real Time Adaptive Filter based EMG Signal Processing and Instrumentation Scheme for Robust Signal Acquisition Using Dry EMG Electrodes

2019 ◽  
Author(s):  
Muhammad Zahak Jamal ◽  
Dong-Hyun Lee ◽  
Dong Jin Hyun
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Adaptive Filter ◽  
Signal Acquisition ◽  
Emg Signal ◽  
Emg Electrodes

On the Application of DSP Technology in Real-Time Voice Communication System

2010 ◽  
Vol 159 ◽  
pp. 727-732
Author(s):  
Wen Ge Feng
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Software Design ◽  
Interface Design ◽  
Design Principle ◽  
Signal Acquisition ◽  
Voice Communication ◽  
Voice Signal ◽  
Signal Acquisition And Processing ◽  
The Voice

This paper describes the advantages of DSP TMS320VC5402 in the voice coding communication and focuses on interface design of real-time voice signal processing as well as the hardware and software design of the system from the aspect of voice signal acquisition and processing. Besides, it also introduces the corresponding design principle of hardware and software.

scholarly journals Development of A Low-Cost Arduino-Based 12-Lead Ecg Acquisition System And Accompanied Labview Application

2019 ◽  
Vol 9 (1) ◽  
pp. 1641-1648
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Low Cost ◽  
Numerical Data ◽  
Digital Signal ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Frequency Noise ◽  
Ecg Signal ◽  
Active Analog

In this paper, the design of a real-time digital multi--channel ECG signal acquisition system is presented. With the purpose of fabrication towards a simple, compact and low-cost tool for bioelectrical signal processing laboratories, the system is developed to acquire the 12 leads EGC signals and converted to numerical data based on an Arduino module named as Leonardo equipped 12 channels ADC. To observe the EGC waves, the ECG signals are amplified through designed amplifiers with the gain of 60 dB. To reduce the effects from the DC component as well as the baseline wandering and the high frequency noise, the active analog bandpass filter ranged in 0,05 Hz to 100 Hz was designed. The power line noise of 50 Hz also decreased with an active analog bandstop filter with attenuation -38 dB. Under the PC application was built using Labview programing, the low-cost digital ECG signal acquisition system was demonstrated with the requirement of observation in real-time. To clarify the small wave in the digital EGG signal, the limitation of the analog signal processing is improved through the digital filters parameterized in the software to increase the SNR from 1.4 dB to 27.6 dB. Practically, the system is evaluated through a series of experiments on a volunteer person resulting the ECG data is recorded and stored in a TDMS file. Since the system is designed as opened-system, a series of developments towards various applications in biomedical diagnosis based on digital signal analysis techniques is promised to be feasible in the near future.

scholarly journals A System for the Synchronized Recording of Sonomyography, Electromyography and Joint Angle

2007 ◽  
Vol 1 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Q.H Huang ◽  
Y.P Zheng ◽  
X Chena ◽  
J.F He ◽  
J Shi
Keyword(s):  
Real Time ◽  
Joint Angle ◽  
Surface Emg ◽  
Diagnostic Tools ◽  
Ultrasound Images ◽  
Emg Signal ◽  
Signal Collection ◽  
B Scan Images ◽  
Emg Electrodes ◽  
Semg Signals

Ultrasound and electromyography (EMG) are two of the most commonly used diagnostic tools for the assessment of muscles. Recently, many studies reported the simultaneous collection of EMG signals and ultrasound images, which were normally amplified and digitized by different devices. However, there is lack of a systematic method to synchronize them and no study has reported the effects of ultrasound gel to the EMG signal collection during the simultaneous data collection. In this paper, we introduced a new method to synchronize ultrasound B-scan images, EMG signals, joint angles and other related signals (e.g. force and velocity signals) in real-time. The B-mode ultrasound images were simultaneously captured by the PC together with the surface EMG (SEMG) and the joint angle signal. The deformations of the forearm muscles induced by wrist motions were extracted from a sequence of ultrasound images, named as Sonomyography (SMG). Preliminary experiments demonstrated that the proposed method could reliably collect the synchronized ultrasound images, SEMG signals and joint angle signals in real-time. In addition, the effect of ultrasound gel on the SEMG signals when the EMG electrodes were close to the ultrasound probe was studied. It was found that the SEMG signals were not significantly affected by the amount of the ultrasound gel. The system is being used for the study of contractions of various muscles as well as the muscle fatigue.

scholarly journals Design and Development of Adaptive filter for Real Time Signal Processing

2021 ◽  
Vol 1074 (1) ◽  
pp. 012036
Author(s):  
Dr Krishna Samalla ◽  
S.P.V SubbaRao ◽  
G Mallikarjuna Rao ◽  
B.N. Jagadeesh
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Adaptive Filter ◽  
Time Signal ◽  
Design And Development ◽  
Real Time Signal Processing

scholarly journals Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

2021 ◽  
Vol 58 (1) ◽  
Author(s):  
Khalil Ullah ◽  
Khalid Shah
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Visual Inspection ◽  
Automatic Detection ◽  
Ground Truth ◽  
Noisy Channels ◽  
Perfect Agreement ◽  
Emg Signal ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.

scholarly journals Real-Time Evaluation of the Signal Processing of sEMG Used in Limb Exoskeleton Rehabilitation System

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Baofeng Gao ◽  
Chao Wei ◽  
Hongdao Ma ◽  
Shu Yang ◽  
Xu Ma ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Training System ◽  
Human Anatomy ◽  
Semg Signal ◽  
Average Delay ◽  
Rehabilitation Training ◽  
Upper Limb Exoskeleton ◽  
Emg Signal ◽  
Exoskeleton Robot

As an important branch of medical robotics, a rehabilitation training robot for the hemiplegic upper limbs is a research hotspot of rehabilitation training. Based on the motion relearning program, rehabilitation technology, human anatomy, mechanics, computer science, robotics, and other fields of technology are covered. Based on an sEMG real-time training system for rehabilitation, the exoskeleton robot still has some problems that need to be solved in this field. Most of the existing rehabilitation exoskeleton robotic systems are heavy, and it is difficult to ensure the accuracy and real-time performance of sEMG signals. In this paper, we design a real-time training system for the upper limb exoskeleton robot based on the EMG signal. It has four main characteristics: light weight, portability, high precision, and low delay. This work includes the structure of the rehabilitation robotic system and the method of signal processing of the sEMG. An experiment on the accuracy and time delay of the sEMG signal processing has been done. In the experimental results, the recognition accuracy of the sEMG is 94%, and the average delay time is 300 ms, which meets the accuracy and real-time requirements.

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