“…The noise generated by the skin layers is at frequencies above 500 Hz [1,2,[8][9][10][11][12][13][14][15][16][17][18][19][20][21] and at those below 10 Hz [1,9,11,12,14,18,20,22]. Some authors consider that there is noise also in higher frequencies, and thus they use filters that cancel up to 20 Hz [2,6,8,13,[15][16][17]19,21,23,24]. Nevertheless, in [7], the authors suppressed frequencies between 90 and 250 Hz and, in [25], the authors removed frequencies lower than 5 Hz and higher than 375 Hz.…”
Section: Signal Acquisitionmentioning
confidence: 99%
“…Hz 10-500 20-450 20-500 25-500 [1] X [9] X [11] X [26] X [23] X [12] X [13] X [2] X [14] X [15] X [16] X [24] X [17] X [18] X [19] X [6] X [20] X [21] X [8] X Another issue found in the study of myoelectric signals is the frequency at which EMG signals should be sampled-a high frequency could give excess noise, and a lower one could lose a vast amount of information. The sampled frequency most commonly used is 1 kHz [1,9,10,12,13,18,19,[27][28][29]. Other authors (e.g., [2,11,22,26,30,31]) use a higher frequency of 1.5, 2, 3, 4, or 10 kHz.…”
“…Generally, for signal classification, more than one signal is required, because every movement is originated from different parts of the muscle and depends on a number of different muscles; therefore, the use of different channels helps to extract as much information as possible from the action(s) performed by the muscle(s). Among the various studies that have been done, it is common to work with four [1,9,13,23,29,38,39], six [19,40,41], or eight [2,7,11,22,30] channels for the acquisition of the signal; some research papers even work with a smaller number of channels [26,42]. Table 3 depicts an abridgement of the number of channels used by different studies and Table 4 summarizes the electrode type used and the place of electrode placement body.…”
Section: Referencementioning
confidence: 99%
“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.
“…The noise generated by the skin layers is at frequencies above 500 Hz [1,2,[8][9][10][11][12][13][14][15][16][17][18][19][20][21] and at those below 10 Hz [1,9,11,12,14,18,20,22]. Some authors consider that there is noise also in higher frequencies, and thus they use filters that cancel up to 20 Hz [2,6,8,13,[15][16][17]19,21,23,24]. Nevertheless, in [7], the authors suppressed frequencies between 90 and 250 Hz and, in [25], the authors removed frequencies lower than 5 Hz and higher than 375 Hz.…”
Section: Signal Acquisitionmentioning
confidence: 99%
“…Hz 10-500 20-450 20-500 25-500 [1] X [9] X [11] X [26] X [23] X [12] X [13] X [2] X [14] X [15] X [16] X [24] X [17] X [18] X [19] X [6] X [20] X [21] X [8] X Another issue found in the study of myoelectric signals is the frequency at which EMG signals should be sampled-a high frequency could give excess noise, and a lower one could lose a vast amount of information. The sampled frequency most commonly used is 1 kHz [1,9,10,12,13,18,19,[27][28][29]. Other authors (e.g., [2,11,22,26,30,31]) use a higher frequency of 1.5, 2, 3, 4, or 10 kHz.…”
“…Generally, for signal classification, more than one signal is required, because every movement is originated from different parts of the muscle and depends on a number of different muscles; therefore, the use of different channels helps to extract as much information as possible from the action(s) performed by the muscle(s). Among the various studies that have been done, it is common to work with four [1,9,13,23,29,38,39], six [19,40,41], or eight [2,7,11,22,30] channels for the acquisition of the signal; some research papers even work with a smaller number of channels [26,42]. Table 3 depicts an abridgement of the number of channels used by different studies and Table 4 summarizes the electrode type used and the place of electrode placement body.…”
Section: Referencementioning
confidence: 99%
“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.
Accurate identification of the intended hand movement from the surface Electromyography (sEMG) data is desired for effective control of myoelectric lower arm prostheses. This study improves the classification accuracy of hand gestures by using feature arrays, Kalman filter (KF), and a Softmax classifier. We use data from BioPatRec database to classify ten hand movements performed by 17 participants. The proposed classifier achieved 95.3% accuracy without KF, and 99.3% accuracy when KF was used to smooth the training data.Clinical Relevance-This feature-based classifier can classify 200k samples per second making it suitable for online implementation.
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