Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal

Wang, Mo; Wang, Xinan; Chen, Peng; Zhang, Sixu; Fan, Zhuochen; Liu, Zhong

doi:10.1007/s11107-018-0809-1

Cited by 20 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main purpose is to determine the moment when the accelerometer is close to static, fully supported on the ground. Using the following Equation (8) to make a preliminary division of gait, a binary function of heel and toe acceleration signals is produced, when the accelerometer is close to the stationary state, the appropriate phase is a flat phase, the binary function is 0, while the non-flat phase is the non-stationary state of the accelerometer and the binary function is equal to 1.…”

Section: Preliminary Segmentationmentioning

confidence: 99%

“…Gait locomotion, as a fundamental activity for all humans, is a cyclic spatiotemporal complex act. Gait information can be acquired by collecting kinematics signals, bioelectrical signals, videos and images [7][8][9][10][11][12][13]. In traditional gait analysis methods, a three-dimensional motion capture (3D Mo-Cap) system and force plate pressure signal can accurately describe the 3D motion of the human body, while can accurately detect the gait event with error of 0.13% [14], which is often called the golden standard method in gait analysis [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

View full text Add to dashboard Cite

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

show abstract

Section: Preliminary Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Analysis of the alterations in human physiology during different locomotion is very important in sport sciences. Due to the changes in leg muscle activation, while doing different locomotions, many works analyzed EMG signals using various techniques (Oliveira et al, 2014;Subbu et al, 2015;Nazmi et al, 2019;Wang et al, 2019). Besides, since brain activation also changes in different locomotions, many studies worked on the analysis of EEG signals using various techniques (Presacco et al, 2011;Maidan et al, 2019;Bodda et al, 2020;Tortora et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

Pakniyat¹,

Namazi

2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.

show abstract

“…In exercise, repetitive/periodic movement can increase the improvement of motor skills and the smoothness of movement progressively. Information extraction from EMG burst can be used to predict the dynamic of muscle strength [13] and the process of muscle toward fatigue [14].…”

Section: A Emg and Eeg Signal Characteristic On Motoricmentioning

confidence: 99%

“…The use of a digital filter approach is a standard method for noise reduction. However, due to EMG signals non-stationary characteristics, applying various digital filter approaches is not the most efficient [14,13,19].…”

Section: Emg Burst Denoising Using Dwtmentioning

confidence: 99%

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

2020

International Journal of Biology and Biomedical Engineering

View full text Add to dashboard Cite

Effective segmentation of electromyography (EMG) burst that synchronizes with electroencephalography (EEG) for long-duration recording is important steps to better understand the quantification of brain-muscle connectivity in periodic motoric activities. The work proposes an alternative automatic EMG segmentation scheme consists of four main steps, i.e. denoising of EMG burst signal using discrete wavelet transform, enveloping signal using time-windows averaging of RMS amplitude, an adaptive threshold to detect start/end burst envelope with accommodation of muscle contraction characteristic and the final step is conversion enveloping signal to binary segmentation signal.The proposed scheme is evaluated to detect contraction period/duration of EMG for the subject under repetitive holding and releasing grasp using a physiotherapy device. During exercise, the bio-amplifier board is customized to acquire simultaneous EEG and EMG from the region of flexor digitorum superficialis (FDS) of muscle and cortical motor of the brain, with total 284 EMG burst that counting by manual segmentation. The automatic segmentation can detect the total EMG burst by 6.25% error of false burst detection.The usefulness of proposed scheme is also tested to association analysis according to the power of EMG burst and the power of mu-wave of EEG recorded on the motor cortex. The changing trend of the power of mu-wave associated with muscle relaxation, muscle contraction strength and the synchronization level on the motor cortex during exercise are analyzed with integrated information that is relevant with biofeedback concept. The results demonstrate that proposed scheme has potential to be an effective method for the evaluation of biofeedback rehabilitation exercise.

show abstract

Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal

Cited by 20 publications

References 14 publications

A Determination Method for Gait Event Based on Acceleration Sensors

A Determination Method for Gait Event Based on Acceleration Sensors

Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

Contact Info

Product

Resources

About