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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.

Magnetically actuated microrobots for such tools have potential accomplish procedures in biological and medical applications. In this paper, a novel magnetically actuated hybrid microrobot with hybrid motion driven by an electromagnetic actuation system has been proposed. An o-ring type permanent magnet is embedded in the hybrid microrobot as an actuator driven by rotational magnetic field which is generated by a 3 axes Helmholtz coils. It is composed by two motion mechanisms. One is the spiral jet motion moved by rotating its body. The other one is fin motion moved by vibrating its body. Because only one permanent magnet is used inside the hybrid microrobot, two motions can be controlled separately without any interference. The hybrid microrobot can change its two motions to realize multi-DOFs movement and flexibility motion. The verified experiments are conducted in the pipe. The experimental results indicate that the moving speed can be controlled by adjusting the magnetic field changing frequency and the direction of motion can be controlled by changing the magnetic field direction.