Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

Abstract

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement.

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

“…These classifiers were selected due to their high performance in classification problems and low computational complexity (Chowdhury et al, 2013), being recommended as robust classifiers in several studies (Chowdhury et al, 2013;Cipriani et al, 2011;Guo et al, 2015;Khushaba et al, 2012;Oskoei and Hu, 2008;Phinyomark et al, 2012a;Wang et al, 2013). In addition, five-fold cross validation with all trials of the experiments was used to assess the performance of the classifiers.…”

“…Several works have used magnitude-based features to feed classifiers to recognize hand motor tasks involving elbow, forearm, wrist and open/close hand movements (Guo et al, 2015;Oskoei and Hu, 2008;Phinyomark et al, 2009). Other systems have got user's commands for a limited number of hand and individual finger gestures (Naik et al, 2010(Naik et al, , 2009Peleg et al, 2002;Tsenov et al, 2006), and even for combined finger movements (Khushaba et al, 2012).…”

“…Techniques based on fractals dimensions (FD) have been used to estimate the non-linear properties of sEMG, which present sensibility at frequency and magnitude to the strength of muscle contraction (Arjunan and Kumar, 2007). Recently, a combination of Higuchi's fractal dimension (HFD) and detrended fluctuation analysis (DFA) were more employed on sEMG signals (Guo et al, 2015), in order to measure the non-linear property in sEMG signals, with the advantages of features from time and frequency domains. Another factor that bother the prosthesis users is the high number of electrodes, as the training with many input channels is a long and hard process, resulting in their decision to use only a limited and very simple prosthesis (2-3 degrees of freedom).…”

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

“…First, the neural network should be trained by the sEMG signals and kinematics variables measured offline before it is applied to online motion estimation, so the estimation performance of the network definitely depends on the training data. Since sEMG is a typical non-stationary real-time signal [23], there certainly exist differences between the new test data and the offline training data, which would bring on estimation errors when the trained network is put into online applications. Normally, the measurement equations in a state-space model will reduce the effects of input errors.…”

Decodificación de Movimientos Individuales de los Dedos y Agarre a Partir de Señales Mioeléctricas de Baja Densidad

“…Más recientemente, Guo et al (2015) han comparado diferentes métodos de características como WP, DFA y MM (Modelos Musculares) combinados con los clasificadores SVM y ANN, del inglés Artificial Neural Network, usando seis canales de datos de señales sEMG. Sin embargo, su estudio no ha incluido tareas de destreza de la mano ni ha incluido sujetos amputados.…”