Design of Rehabilitation System Based on Research of Motion Classification and Virtual Reality Technology - Li, Qingqing (fi=Turun yliopisto|en=University of Turku|, 2018)
Surface electromyography (sEMG) is a composite effect of superficial muscle fibers and nerves on the outer surface of the skin. Using skin surface EMG (sEMG) to predict gesture is one of the important subjects in related research area, it can be widely applied in the field of prosthetic control, rehabilitation medicine and so on. In this thesis, the EMG signal data was collected by theMYO armband, and the signal was wavelet-transformed to reduce the noise and then divided by moving average methods to extract the signal data of each action. Through widely-validated phonogram techniques in phonetics, each gesture signal data is converted into a spectrogram which contains timing frequency distribution. The convolutional neural network(CNN) use spectrogram to learn and generate a learning Model. This thesis also tries to use the open source Tensorflow framework to build a neural network to train the raw data and learn a model. Due to the Tensorflow’s operation efficiency and its good adaptability in multiple platforms, Tensorflow can generate a model with good portability. At last, a mirror therapy(MT) VR rehabilitation system was developed based on MYO armband, HTC virtual reality helmet and the trained Tensorflow model. The original signal is collected from the arm of the patient through the MYO armband, and then sent signal to the Python processing program for feature extraction. The extracted features are processed to the trained model and the model make gesture prediction. The HTC helmet is used to display the corresponding gesture animation of the prediction results. VR helmets offers an immersive experience by creating a good mirroring environment which helps generate a better treatment outcome. In this thesis, a signal segmentation method which can remove redundancy data is introduced. After the model was generated, the thesis compares the differences of the two models. According to the test results, the accuracy of the spectrogram-based method is higher with higher hardware requirements and lower process speed. However, the method based on feature vector is faster with lower accuracy.