# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification
### SVM-Based Myoelectric Signal Classification  
Many researchers have extensively discussed resolution methods for pattern-based classification for control applications. This review only includes works related with SVM, since this method is widely recommended by several authors; this is largely because it is very flexible and can be combined with other methods, which allows improving the accuracy of classification. For example, in [61], forms of Motor Unit Potentials (MUP) in a Motor Unit Potential Train (MUPT) are evaluated to determinate if they represent a single motor unit They authors obtained 95.6% accuracy with this method. By taking advantage of technological advances, in [32], the Myo Armband device from Thalmic Lab was placed on 26 subjects to perform a series of four hand gestures, and a linear kernel was used for obtaining an average accuracy of 94.9% with eight electrodes and 72% with four. Similarly, in [54], a DL-3100 system was used to measure MES signals as a new user authentication method for mobile devices. SVM were trained under four features values (max, min, time of max and min value) and it is possible to choose between five hand gestures to unlock the mobile device. However, in [20], only two channels were used to classify five classes of hand movements, and a method for normalize the signals was implemented, reaching 95% accuracy precision with an expert user. In [13], two different kernels (Gaussian and RBF) were used to classify five different leg movements, through four MES channels, by combining MAV, WL, ZC and, SSC in such a way that in total 16 different vectors are introduced to SVM. With this method (MKL-SVM), the authors obtained more than 90% of accuracy. In the same manner, in [12; 51], an RBF kernel was used for the SVM classifier to perform the error estimation using Leave-One-Out Cross Validation (LOOCV) to separate between six different walking movements; however, the former used 31 electrodes placed on different leg and buttocks muscles to form 16 bipolar signals, while the latter only used 9 electrodes. Both extracted MAV, ZC, WL and, SSC feature signals, but, in [12], the authors also extracted RMS, AR1, AR2 and, AR3. In both studies, 95% precision was obtained. In time domain, [PERSON] and [PERSON] [49] combined discriminant analysis with SVM to distinguish between four arm movements. When extracting MAV and AR from windows formed by 32 samples at a sampling frequency of 1 kHz, they made the features vector, with which they obtained an average accuracy of 99%. Additionally, [PERSON] [22] used AR6, MAV, ZC, WL and SSC to classify six different movements, taking the signals readings in the forearm, by means an incremental learning adaptive algorithm to SVM, which incorporated useful information in tests to a self-correction mechanism to suppress erroneous classifications, with 96.6% average accuracy. In addition, some studies use MES signals focused on support for people with disabilities. For example, [PERSON] et al. [45] studied the navigation of an Electric WheelChair (EWC). Four channels of MES signals, placed on cheek, neck, and shoulder, control the seven EWC movements, the iMES of each channel was calculated to form the feature vector. The average classification accuracy was 89.7%. In the same manner, [PERSON] et al. [53] took advantage of signals in time domain, therefore they did not use any method for feature extraction. Besides, they combined the information about signal history through the HMM (Hidden Markov Models) with the advantages of time-independent SVM classification, forming an HMM-SVM classification algorithm with 91.8% accuracy to distinguish six different arm movements. [PERSON] et al. [24] implemented visual feedback from the virtual prosthetic hand system to improve classification accuracy and achieved a mean of 98.79%. The authors distinguished between eight movements, with three pairs of sensors, by the SVM classifier and compared the obtained results by using RMS, MAV, VAR, WL, WAM, IAV and SCC as a different features vector.