In recent years, the analysis of surface electromyography (sEMG) signals by feature engineering and machine learning has developed rapidly. However, when feature engineering is applied to feature extraction of sEMG signals, important feature information in the signals will inevitably be omitted, which will reduce the performance of signal analysis and recognition. Therefore, this paper proposes a method to complete classification of sEMG hand movements based on convolutional neural network (CNN) and stacking ensemble learning. In this method, a primary classifier based on CNN is designed to extract sEMG data features, which avoid omission of important feature information. A secondary classifier based on the stacking method is designed to integrate three primary classifiers trained with time domain, frequency domain and time-frequency domain data of the sEMG signal respectively. Then, several experiments on NinaPro DB5 dataset is performed to evaluate the proposed models. When the window length is 200ms, primary classifier is trained and tested with the sEMG signal data divided by the 80ms, 100ms, and 125ms sliding length. The best accuracy can reach 71%. The primary classifier and the secondary classifier trained and tested with sEMG signal data divided by window lengths of 200ms and 300ms in the case of a sliding length of 100ms. When the window length is 200ms, the best primary classifier accuracy and the best secondary classifier accuracy can be 70.92% and 72.09%, respectively. On the window length of 300ms, the best primary classifier accuracy and the best secondary classifier accuracy can reach 75.02% and 76.02%, respectively. Finally, the model designed is compared with Linear Discriminant Analysis (LDA), Long Short Term Memory-CNN (LCNN), Support Vector Machine (SVM), and Random Forests. Under the same conditions, the average accuracy of the secondary classifier is 11.5%, 13.6%. and 10.1% higher than LDA, SVM, and LCNN in p11, respectively. Also, the average accuracy rate is 3.05% higher than SVM and Random Forests in p7.

Thanks to the powerful capability of the feature extraction, deep learning has become a promising technology for an increasing number of researchers to decode movements from surface Electromyography (sEMG) signals. The mainstream methods of deep learning are based on Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN). However, the sequential features of sEMG signals will be ignored in CNN-based approaches while the training of the neural networks is much time-consuming in RNN-based approaches. To solve these problems, a novel convolutional vision transformer (CviT) with stacking ensemble learning is proposed in this paper, which has great potential in the fusion of sequential and spatial features of sEMG signals with the parallel training. In NinaPro DB2, the proposed method achieves 80.02% with the window length of 200ms. In the subset of NinaPro DB2 (Exercise E1), the proposed method achieves 83.47% and 84.09% with the window length of 200ms and 300ms respectively. In the subsets of NinaPro DB5 (Exercise A, Exercise B), the proposed method achieves 76.83% and 73.23% respectively. The experimental results demonstrate that the proposed CviT has better performance than most current approaches. In addition, the successful application of Transformer in sEMG-based movements classification provides a significant reference for the application of Transformer in other biological signals.

With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.

As a promising technology in human-computer interface (HCI), deep learning has good application prospects for gesture recognition from surface electromyography (sEMG) signals. However, mainstream network structures with the satisfactory recognition accuracy are too complex to be deployed on edge devices. And the lightweight neural network tends to have difficulty in fulfilling the requirement of the recognition accuracy with limited computing power. In order to make the neural network adapt to different applications as much as possible, we propose a flexible and modular method based on sEMG and acceleration signals for gesture recognition. Moreover, a lightweight convolutional neural network with the improved channel attention mechanism (ICA-CNN) can achieve satisfactory performance in recognition accuracy or inference speed for different applications. The experimental results show that the recognition accuracy of the proposed method on 49 gestures is 94.24% using sEMG and acceleration signals, much higher than the accuracy of the method only using sEMG signals. In addition, the single inference of the proposed method takes only 38.6ms on the CPU.

In recent years, gesture perception has become crucial to human-computer interaction technologies. Among various techniques, gesture recognition based on surface Electromyography (sEMG) signals has gained significant prominence, with deep learning methods playing a pivotal role in this domain. However, as the demand for accurate gesture recognition continues to rise, there is a growing inclination toward selecting complex deep neural network architectures. This trend, however, poses challenges in terms of performance and runtime requirements for computing devices. This paper introduces a novel gesture recognition method utilizing the MLP-Mixer framework combined with Stacking ensemble learning to address these challenges. The proposed method effectively captures the features of sEMG data by employing simple multi-layer perceptrons (MLP), achieving a level of accuracy comparable to complex networks while simultaneously reducing inference time. Experimental results demonstrate that the method performs a classification accuracy of 80.03% and 81.13% for 49 actions in the open-source dataset NinaPro DB2, using window lengths of 200ms and 300ms, respectively. Furthermore, the method achieves a single inference speed of 54.77ms with a window length of 200ms. In NinaPro DB5, with window lengths of 250ms and 300ms, the method presented in this paper achieves accuracy rates of 73.39% and 74.82%, respectively, completing inference within just 11.45ms using the 300ms window length. Notably, the technique also demonstrates its ability to mitigate the impact of individual differences in sEMG data on recognition accuracy.