| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 107 === In recent years, the application of surface electromyography (sEMG) has increasingly more prominent, while the development of deep learning algorithms cannot be ignored. Therefore, within the field of sEMG-based pattern recognition, more and more AI algorithms are employed. However, many results can not demonstrate high performance. This is due in part to a large amount of data required for the deep learning algorithm. In this paper, a deep neural network that combined 3D-convolutional layers and a long short-term memory layer (LSTM) is proposed. Meanwhile, we propose data augmentation methods and a transfer learning algorithm to improve our performance. In this paper, two datasets are used to evaluate experimental results. The first dataset is an open dataset that is comprised of 11 abnormal and 11 normal participants of the lower limb. The second dataset that referred to as the pre-training and target dataset consists of 28 and 5 examples. This proposed method is shown to outperform the other networks in sEMG-based lower limb abnormal recognition. The experiments with 94.12% accuracy show that our method is effective for lower limb abnormal recognition.
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