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.
Highlights
With the development of electromyography (EMG) techniques, the surface EMG signal, which reflects the activation level of skeletal muscles, has become an advanced tool for intuitive control of prostheses or robot arms
For the performance of recognition accuracy rate, the results show that the combination of WP and Neural Network (NN) obtained the highest rate and the combination of Root Mean Square (RMS) and Support Vector Machine (SVM) obtained the lowest rate
Eight combinations of four feature extraction methods and two classifiers were tested to recognize eight human upper-limb motions, using features extracted from surface electromyography (sEMG) signals as inputs
Summary
With the development of electromyography (EMG) techniques, the surface EMG (sEMG) signal, which reflects the activation level of skeletal muscles, has become an advanced tool for intuitive control of prostheses or robot arms. Fukuda et al [1] used sEMG signals to control a manipulator They adopted a statistical neural network, named the log-linearized Gaussian mixture network, to achieve robust discrimination between differences among individuals, electrode locations, and time variations caused by fatigue or sweat. They reported that the method can provide smooth control for the manipulator and it might allow a physically handicapped person to sense a feeling of prosthetic control similar to that of the original limb. An AR model-based multi-motion recognition method was proposed to classify different upper-limb motions to control an upper-limb exoskeleton rehabilitation device [4,5]
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