Figure 4 - uploaded by Igor Stirn
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Energy envelope of the rectified EMG signal of LD1 of three consecutive muscle contractions during three consecutive arm strokes. Active and inactive parts of the muscle contractions are marked.
Source publication
The aim of our study was to compare mean frequency (MNF) decrease in some upper body muscles during a 100-metre all-out crawl by using two different normalization methods: firstly, when MNF at the end of swimming (MNFSend) was normalized with respect to its initial value at the beginning and expressed with a fatigue index (FI) labelled FIstart, and...
Contexts in source publication
Context 1
... raw EMG signals (swimming and isometric) were filtered using the 5th order Butterworth band-pass filter with the lower and upper cut-off frequencies set to 10 Hz and 500 Hz respectively. The active phase of the muscle during the arm stroke was determined individually for every stroke and muscle as illustrated in Figure 4. Firstly, the energy envelope of the rectified EMG signal was calculated using a sliding data window of length 250 ms (equation 1). ...
Context 2
... raw EMG seg- ments belonging to the active phases were extracted and used for calculation for amplitude and frequency analysis of the EMG. The inactive phase (IF) was defined as the time interval between the two successive active phases, as shown in Figure 4. The power spectral density (PSD) for each segment was estimated using the periodogram method (Proakis & Manolakis, 1996) and mean frequency (MNF) was used as a measure of central Analysing data during isometric contraction. ...
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ABSTRACT
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Citations
... Some researchers achieve an efficient noise reduction using the reflected envelope. A drawback is that it often causes unacceptable bias errors [10], [11] and does not prevent spikes. Therefore, filtering technics are involved. ...
... The modified state-space model (11) and (15) has now timecorrelated and white w i andv i and the KF can be applied, if to derive the optimal bias correction gain taking into account ...
... To provide a near optimal estimate, this filter requires an averaging horizon [m, i] of N points, from m = i−N +1 to i, to be optimal N opt in the MSE sense . Since w i andv i are both zero mean and their correlation does not produce bias, then it follows that the UFIR filter can be applied directly to (11) and (15), unlike the KF. ...
Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.
... The modified state-space model (11) and (15) has now timecorrelated and white w i andv i and the KF can be applied, if to derive the optimal bias correction gain taking into account ...
... To provide a near optimal estimate, this filter requires an averaging horizon [m, i] of N points, from m = i−N +1 to i, to be optimal N opt in the MSE sense . Since w i andv i are both zero mean and their correlation does not produce bias, then it follows that the UFIR filter can be applied directly to (11) and (15) . . . ...
... The most widely used standard techniques developed for the MUAP envelope extraction employ the mean square value (MSV) criterion [4], waveform produced by the rectified signal [15], [16], and the Hilbert transform [17]. Since these methods do not completely remove artifacts and ripples, the output is sometimes smoothed that, however, may yield unacceptably large bias errors and does not prevent spikes [18], [19]. The envelope can be improved using the Savitsky-Golay smoother combined with a low-pass filter [20], but only if the time-delay-lag causing EMG is not an issue. ...
Accuracy and stability of the myoelectric prosthetic arm digital proportional control strongly depends on shape of the rectified motor unit action potential (MUAP) envelope. To mitigate effects caused by the envelope-induced torque ripples, mechanical vibrations (TRMV), and electromechanical delays (EMD), in this paper we develop in discrete-time state-space the unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory recursive H∞ filter. The filters developed are called the cUFIR filter, cKF, and cH∞ filter and applied to extract a smooth near Gaussian MUAP envelope by viewing ripples as colored measurement noise (CMN). Additional UFIR and Rauch-Tung-Striebel (RTS) smoothers are also used to improve the performance when the EMD is acceptable. Based on experimental examples of prosthetic arm control, it is shown that the advanced cUFIR filter, cKF, and cH∞ filter improve the performance of the original filters by the factor of about 10 and essentially reduce the TRMV and EMD.
... The Hilbert Transformation is a mathematical algorithm to provide the EMG signal envelope. The Hilbert transformationû n (imaginary part) of u n (real part) can be used to draw the envelope U n = u 2 n +û 2 n , as shown in Fig.1, available from (24), where desired (smoothed) envelope is required due to the data are highly contaminated by noise (15). Several important observations can now be made by analyzing Fig. 1 The problem focuses on identifying the best processing algorithm achieving the highest estimation, in order to increase the accuracy of the features. ...
... The decreasing of the median frequency will indicate the muscle fatigue. According to previous research, conduction velocity (CV) measured velocity of action potential spreads throughout muscle fibers and the decreasing of the muscle conduction velocity causes the median frequency to decrease (Cifrek, Medved, Tonković, & Ostojić, 2009;Stirn, Jarm, Kapus, & Strojnik, 2013). ...
Balance training devices such as wobble board, basu ball and balance cushion are the tool use in balance training exercise programme in order to improve muscle strength and restore posture balance due degeneration of body function or injury. Recently, self-balancing scooter such as Segway and hover board showed a positive effects on rehabilitation. However, it is less known how these devices affect muscle physiological properties. This study aims to to measure ankle muscles activation on difference balance training devices and hover board. Besides, a comparison between these device will be done in order to identify if hover board has a promising feature to be an alternative balance training device. In this research, surface EMG (sEMG) was used to record tibialis anterior and gastrocnemius muscle activities. Seventeen healthy subjects were required to stand on three different types of balance training device such as wobble board, balance cushion, bosu ball and a hover board. They were asked to maintain their standing position on each devices for two minutes. Both time domain and frequency domain analysis were used to identify the features of the EMG signal. Time domain analysis measurement involved average rectified value (ARV) and root mean square (RMS), meanwhile for frequency domain, median frequency (MDF) of the signal were measured. The results shows that, the RMS is differed significantly between the balance training devices (p<0.05) for tibialis anterior muscle but not gastrocnemius muscle. Meanwhile, no significant difference between the devices in the ARV and the MDF value (p>0.05). Besides, less stable devices increased muscle activity were observed. There is not much difference between hover board and the other devices in term of physiological effects of both tibialis anterior and gastrochemious muscle. It is also suggested that hover board offers a promising feature to be an alternative device for balance training device.
... The TB muscles are mostly used in body propulsion during horizontal movements, such as swimming [10][11][12][13][14][15], skiing [16], and propulsion of wheelchair [17][18][19]. The TB is located in the upper arm of the human body and consists of three heads, (i) the lateral head, (ii) the medial head and (iii) the long head. ...
... These researchers further concluded that fatigue is not solely responsible for changes in sEMG amplitude and frequency. In [12], the researchers introduced a fatiguing protocol immediately after 100-m front-crawl swimming and this fatiguing protocol consists of isometric exercise that isolates a muscle to observe effect of front-crawl swimming only on that particular muscle. This researcher found that the minimum mean frequency is independent of the fatiguing protocol, conditions and intensity of exercise . ...
... Water proof electrodes were not used. [12] To compare decrease in MPF of upper body muscles during 100-m all out front-crawl with different normalization techniques. ...
Objective
The objective of this research was to summarize and analyse the research findings regarding analysis of fatigue in the human triceps brachii (TB) muscle through surface electromyography (sEMG) observations.
Methods
We systematically searched through five major online scientific databases namely the PubMed, Science Direct, Wiley Online, Springer Link and SCOPUS databases, for articles written in the English language from the year 2001 to March 2017. We specifically searched for the words/phrases “surface electromyography” OR “sEMG” AND “muscle fatigue” AND “triceps” in the title, abstract and keywords to narrow our search and identified 291 articles, of which, 52 were found potentially the most relevant.
Results
Of 52 considered articles, 11 analysed fatigue in sports, 11 investigated rehabilitation, 15 considered exercises or trainings, 5 used TB as a co-activator or antagonist, and 5 contemplated elbow extension movements. In addition, 4 of the articles investigated both elbow flexors and extensors and 1 studied training effects in rehabilitation.
Conclusion
Although, many studies in this particular field have considered the TB, further investigations are required to explain some specific facts about fatigue in the TB. The compensation strategy that muscles use to overcome fatigue, the stabilization, overcoming of errors during fatigue along with effect of mental load on brachii muscles and the effect of sports drinks and other eatables on fatigue are a few potential zones that require further in-depth research. This study will guide and direct new researchers to areas that remain hidden.
The effect of ship navigation radar signal processing has a great impact on the overall performance of the radar system. In this paper, the signal processing algorithm is studied. Firstly, the principle of radar azimuth and distance monitoring is introduced, then the pulse accumulation algorithm and median filtering algorithm are analyzed, and finally a sea clutter suppression algorithm based on sensitivity time control (STC) and a rain and snow clutter suppression algorithm based on constant false alarm rate are designed in order to improve the target monitoring performance of radar. In the test of the algorithm, the radar signal processing algorithm designed in this paper has good precision as monitoring error of the target's azimuth and distance is controlled within 1%; and it also has a better suppression effect of sea clutter and rain and snow clutter, which can suppress the clutter well, improve the target clarity, and ensure the safe navigation of the ship. The experiment proves the effectiveness of the proposed algorithm and provides some theoretical basis for the better processing of radar signals, which is beneficial to improve the environment perception ability of ships in harsh environments and promote the further development of the navigation industry.
There are no in-depth biomechanical studies about swimming because detailed analyses of motion and muscle activity in water are difficult. In the present study, we measured the activity of trunk and leg muscles during the flutter kick, using a waterproof, wireless electromyographic system. Flutter kick movements were recorded by a waterproof high-speed camera, and were divided into four phases to analyze muscle activity in each phase: (1) hip extension-knee extension, (2) hip extension-knee flexion, (3) hip flexion-knee flexion, and (4) hip flexion-knee extension. The femoral biceps and gastrocnemius became active from Phase 1 to 2, the rectus femoris and vastus medialis became active from Phase 3 to 4, and the internal oblique muscle activity was high from Phase 2 to 3. In this study, the muscle activity patterns during the flutter kick were described. It was suggested that for this kick the internal oblique muscle has an important role during the switch over from hip extension to flexion.
