Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction

Abstract

Surface electromyography (SEMG) signals can provide important information for prosthetic hand control application. In this study, time domain (TD) features were used in extracting information from the SEMG signal in determining hand motions and stages of contraction (start, middle and end). Data were collected from ten healthy subjects. Two muscles, which are flexor carpi ulnaris (FCU) and extensor carpi radialis (ECR) were assessed during three hand motions of wrist flexion (WF), wrist extension (WE) and co-contraction (CC). The SEMG signals were first segmented into 132.5 ms windows, full wave rectified and filtered with a 6 Hz low pass Butterworth filter. Five TD features of mean absolute value, variance, root mean square, integrated absolute value and waveform length were used for feature extraction and subsequently patterns were determined. It is concluded that the TD features that were used are able to differentiate hand motions. However, for the stages of contraction determination, although there were patterns observed, it is determined that the stages could not be properly be differentiated due to the variability of signal strengths between subjects.

Muscle Recruitment and Coordination following Constraint-Induced Movement Therapy with Electrical Stimulation on Children with Hemiplegic Cerebral Palsy: A Randomized Controlled Trial

“…Root mean square (RMS), Integrated EMG (iEMG), cocontraction ratio (CR) (CR = iEMG of wrist flexors / [iEMG of wrist extensors + iEMG of wrist flexors] ×100%) during MIVC were recorded and analyzed[ 27 – 32 ]. RMS approaches the quantification of the surface EMG signal by squaring the data, summing the squares, dividing the sum by the number of observations, and finally taking the square root[ 27 – 29 ]. The iEMG reflects the total discharge of motor units involved in movement and the discharge of each unit during a certain time[ 27 – 29 ].…”

“…RMS approaches the quantification of the surface EMG signal by squaring the data, summing the squares, dividing the sum by the number of observations, and finally taking the square root[ 27 – 29 ]. The iEMG reflects the total discharge of motor units involved in movement and the discharge of each unit during a certain time[ 27 – 29 ]. CR could reflect the coordination and activation of agonist and antagonist muscle groups[ 30 – 32 ].…”

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

“…Nadzri et al [10] applied a low-pass filter with a cutoff frequency 6 Hz, Kendell et al [41] used a 6th order low pass with a cutoff frequency of 5 Hz and Al-Angari et al [42] used a band pass with a cutoff frequency from 5 Hz to 500 Hz. Other studies reduced the noise in EMG signals using a high-pass filter with a 500 Hz cutoff frequency to reduce motion artifacts and a low pass filter of 20 Hz cut-off frequency [13].…”

“…A non-invasive technique is applied by placing electrodes or sensors directly on the skin while an invasive approach is penetrating the needle/wire electrode into the muscle tissue to detect and record EMG signals. Notably, the non-invasive technique is preferred to measure EMG signals as this approach is free of discomfort and gives minimal risk of infection to amputees [9,10,11,12]. For surface EMG signals, the amplitude is in a range between 0 to 10 mV and the frequency range is restricted from 10 to 500 Hz.…”

Surface Electromyography of Wrist Flexors and Extensors in Children With Hemiplegic Cerebral Palsy

“…Surface electromyography (EMG) is a method of measuring the temporal signal of bioelectrical activities of the neuromuscular system as recorded from the muscle surface. It has been applied in the measurement of hand activities [5] and in the diagnosis of childhood hypertonia [6]. These studies pointed out the potential clinical application of surface EMG in children with CP, which deserves further elucidation.…”