Identification of 7 Movements of the Human Hand Using sEMG - 360° on the Forearm

This document shows the identification of 7 gestures (movements) of the human hand from sEMG – 360° signals on the forearm. sEMG – 360° is the sEMG measurement through 8 channels every 45° making a total of 360°. When making a hand gesture, there will be 8 independent sEMG signals that will be used to identify the gesture. The 7 gestures to identify are: relaxed hand (closed), open hand (fingers extended), flexion and extension of the little finger, the ring finger, the middle finger, the index finger, and the thumb separately. One hundred samples for each gesture were captured and 3 feature extraction methods were applied in the time domain: mean absolute value (MAV), root mean square value (RMS) and area under the curve (AUC). A vector support machine (SVM) classifier was applied to each extractor. The gestures were identified and the percentage of accuracy in the identification was calculated for each extractor + SVM classifier using the confusion matrix method and including the 8 channels for each gesture. An accuracy of 99.52% was achieved for the identification of the 7 gestures applying sEMG – 360°.


INTRODUCTION
Electromyography is a technique that measures the bioelectrical signals that muscles generate when the body performs an action; these signals have been used for the performance analysis of athletes, for the remote control of highly complex mechanical and electronic systems in movement, to measure development in rehabilitation, among other applications. sEMG signals are acquired by means of surface electrodes.
The literature reports the use of 1, 2 and up to 3 electrode arrays using the RLD (Right Leg Driven) configuration. The commercial device called Myo Armband, Thalmic labs [1] , is a wireless electronic bracelet con- In recent years, different researches have considered sEMG signals to identify the intention of movement of the human hand and to be able to reproduce them in a robotic hand. These signals are acquired when different gestures (movements) are made and then they are processed to acquire the most important information of the signal and once processed they can be entered into a classifier in order to identify the movements made by the user.
Tavakoli et al. [2] reports the sEMG signals of the fore- Another study presented by Shi et al. [3] , where superficial electrodes are used to acquire the sEMG signals from the forearm when performing 4 gestures such as: closed hand, extended index finger, extended thumb, and the 4 extended fingers together. An extraction of characteristics in the time domain was applied, where the main descriptors were: the MAV, the zero crossing (ZC), the slope sign change (SSC) and the waveform length (WL), these characteristics were used in the nearest neighbor classifier (KNN) where they obtained an accuracy of 94%.
In the study presented by Krishnan et al. [4] , the Myo Armband device is used to acquire EMG signals by performing 5 movements of the human hand. The author used some feature extraction methods in the time domain as the simple square integral (SSI), the maximum value and the minimum value, the average frequency and the average potential; these methods were used in an SVM classifier where they obtained an identification accuracy percentage of 92.4% for one user and 84.27% for another user.
In Mukhopadhyay et al. [5]  In the study presented by Sanchez et al. [6] , the author obtained sEMG signals by using the Myo Armband bracelet to predict 8 hand gestures to reproduce on a robotic hand. They used an extraction of characteristics in the time domain as MAV, the RMS, the WL, the mean amplitude change (AAC), the integrated EMG value (IEMG) and the absolute standard deviation (DASDV); these data were used in an extended associative memory (EAM) classifier and obtained an accuracy of 94.83% when using the MAV and RMS extractors.
(1)  From the previous review it can be seen that there are different articles on the identification of some hand gestures using different extractors and classifiers [7] with 1, 2, 4, and 8 array electrodes but with different

Myo Armband bracelet
It is an electronic device developed by the Thalmic Lab company as illustrated in Figure 1 [1] . It is a bracelet that can be placed on the forearm to record the sEMG activity that is generated by the movement of the muscles. This device has 8 arrays of 3 dry-surface electrodes to monitor sEMG, it combines an accelerometer, a gyroscope, a magnetometer, an ARM Cortex M4 processor, indicator LEDs, motor vibrators, Bluetooth communication and a rechargeable power supply. : Where T sampling is the sampling time, Hz is the sample rate and ms equals milliseconds. This indicates that every 5 ms, the EMG signals of the 8 channels are acquired in 360º configuration of the Myo Armband bracelet.

Acquisition of EMG signals
The Myo Armband device was used to acquire the 8 sEMG signals (8 channels [8] , the root mean square (RMS) method [9] and the area method under the curve (AUC) [6] .
In the MAV method, the mean value of the sEMG signal is calculated. The formula to calculate it is shown in Equation 2.
Where N represents the number of data contained in the sample and Xi the data contained in said sample.
The RMS method obtains the root mean square value of the sEMG signal. The mathematical expression is shown in Equation 3.

Classifier
Once the feature extraction has been carried out using each of the proposed extraction methods, a classifier is required to be applied to separate the information and identify each movement. For this study, the support vector machine classifier (SVM) is used, where 100 data of each of the predefined gestures will be used. The data used for SVM can be more or less than one hundred, according to literature, one hundred is a representative number when statistics is applied [10] [11] [12] ; the SVM classifier is an algorithm that can determine a plane that separates the acquired data set into several sets (vectors). For a new set, the similarity of the vector is determined, and it is classified within the set associated with that vector. The kernel functions are represented as: Where x and y are the data that is entered into the classifier, b is a parameter to improve performance and n is the degree of the polynomial, γ defines how much influence a single training example has.
In this work, the 3 types of kernels were applied for the SVM in order to determine which kernel maximizes the percentage of accuracy in the identification of movements.

Confusion matrix
In order to calculate the percentage of accuracy that exists during an identification process, the confusion matrix is used.
In the confusion matrix, each column represents the number of predictions for each class, and the rows represent the instances of the real class, so it is possible to observe the successes and errors of the model during the identification of the movement [13] . Figure 2 shows a confusion matrix for 4 classes: A, B, C and D.

RESULTS AND DISCUSSION
Since each gesture has 8 associated sEMG channels, it is ideal to find the relationship of the 8 channels with the gesture. One technique used is to graph the data in an eight-dimensional space (number of channels). To simplify and show graphically this relationship, Figure   3 shows   The behavior was calculated for the CUA and RMS extractors. There were no significant differences between them.
Once the MAV, AUC and RMS feature extractors were applied to the sEMG signals, the SVM classifier was applied. Figure 4 shows the classification carried out by the SVM with a linear kernel of the data obtained from the MAV for channels 1 and 6. It is observed that there are some data that were misclassified since they are in the region of a different class. Considering the classification obtained from the classes with 100 samples for each gesture, the corresponding confusion matrix was obtained ( Table 3).
70% of the data was used for training and the remaining 30% for testing. Table 2 was obtained for a linear kernel SVM. (1) (1)  Therefore, this research shows that s-EMG-360 improves the accuracy of identification though it is used with the common feature extraction method (RMS, MAV, AUC) and SVM classifier.

CONCLUSIONS
The results show that the RMS extractor in conjunction with the SVM classifier is the combination with the best percentage of accuracy (99.52%) in the identification of the 7 proposed movements of the fingers of the human hand.
It is shown that using a Myo Armband device is a good option for the implementation of the sEMG-360 method.
The sEMG-360 method improves the identification accuracy of human hand gestures. The SVM classifier was the second principal parameter to improve identification accuracy.
It is concluded that the feature extraction methods in the time domain (MAV, AUC and RMS) give the same result for identification accuracy, with RMS having a slight advantage.
At last, we concluded that sEMG-360 around the forearm is a powerful technique because it adds 8 sEMG to the processing data for gesture identification. projects.