In order to solve the real-time control problem of hand rehabilitation exoskeleton robot, a motion angle decoding model was proposed based on surface EMG signal and synchronous motion angle value. The long short-term memory neural network was used to construct the hand motion angle decoding model. During recognition, EMG signal and synchronous angle signal are sent to the model for decoding, and the output of the model is the angle prediction value after 200ms. The experimental results show that the combination of motion angle signal and EMG signal can significantly improve the decoding ability of the model.
This paper introduced the development of a hand rehabilitation therapy system based on virtual reality technology aimed at encouraging stroke patients with hand movement disorders to undertake rehabilitation exercises. The system uses Leap Motion controller, Unity3D development platform and Visual Studio2019 integrated development environment to design, including three modules: game module, interaction module, adaptive module. The game module includes physical simulation, collision detection, audio-visual feedback and other functions, which can better immerse patients in the virtual environment. The interaction module realizes the function of collecting and transmitting the hand movement information and interacting with the game module. According to the patient's performance, the adaptive module, such as game time and object size, can adjust the internal parameters of the system reasonably and appropriately, so as to match the task with the patient's ability. The system has the characteristics of self-adaptation, strong immersion and simple deployment, which can improve the fun of training and the sense of gain of players, and lay the foundation for stroke patients to carry out hand function rehabilitation training at home.
KEYWORDS: Electroencephalography, Feature extraction, Education and training, Detection and tracking algorithms, Machine learning, Data modeling, Signal processing, Deep learning, Brain-machine interfaces, Matrices
Aiming at the problems that the traditional classification and recognition methods of left and right-handed motor imagery EEG signals require prior knowledge and feature extraction requires manual design, the process is cumbersome, and the recognition accuracy is not high, A one-dimensional CNN-LSTM network model that can automatically learn signal features is proposed based on the public motor imagery dataset. The CNN-LSTM network model uses a one-dimensional CNN network to automatically learn and extract the deep-level features of EEG time series, and send the feature sequence to the LSTM classifier for classification. The recognition accuracy of the proposed algorithm is 93.57%. Compared with other algorithms, the proposed algorithm can obtain higher recognition accuracy, and at the same time, it can omit the tedious data preprocessing and feature extraction steps. The proposed method is of great significance to the research on brain-computer interface recognition algorithms.
KEYWORDS: Control systems, Education and training, Signal processing, Sensors, Neural networks, Motion models, Electromyography, Design and modelling, Electrodes, Motion detection
At present, the hand rehabilitation training system mainly adopts passive rehabilitation training method, and the training mode is relatively simple, which cannot reflect the movement intention of patients. This paper has designed and produced a kind of predictive control based on the methods of electricity hand rehabilitation training system, the system can according to your hand on the multi-channel sEMG predict intentions and movement angle, and then drive the exoskeleton robot assisted hand movement, as reflected in training patients' movement intentions, to realize active rehabilitation training. In order to achieve the compliance of the control and prevent the secondary injury to patients, this paper designed the exoskeleton manipulator sliding mode control method. The simulation results and experimental results verify the correctness of the design. The sEMG acquisition and prediction system can accurately predict the motion intention of patients, and the steady-state error of the final control can be kept within 5 degrees, with good accuracy and reliability, which is expected to be applied in the hand rehabilitation training of stroke patients.
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