Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features.

Electromyography (EMG) signals find wide applications in the fields of medicine, sports, and rehabilitation. However, the collection of EMG signals is a laborious process constrained by ethical limitations, resulting in a scarcity of available data and limiting its applicability in various tasks. This study aims to address this limitation by employing generative techniques to synthesize high-quality and diverse EMG signal samples and evaluating their classification accuracy through gesture recognition tasks. We propose an improved approach based on Diffusion Probabilistic Models (DDPM), specifically tailored to the characteristics of EMG signals and the requirements of low-data setting. Our approach employs a conditional generative method, leveraging diffusion models and introducing a patch-based training strategy. Additionally, we have improved the network architecture of DDPM to better suit the task of few-shot EMG signals generation. We evaluate the performance of the generated EMG signals in gesture recognition tasks using the Ninapro DB4, DB5, BioPatRec DB2, and DB3 datasets. The results indicate that when using 20% of the available data for training and generating the same amount of synthetic samples, the average classification accuracy of the generated data reached 94.31% of that of the real data. These findings demonstrate the effectiveness of our approach in modeling the distribution of real data and generating usable synthetic data. Moreover, the addition of synthetic data to the training set significantly enhances classification performance, further validating the efficacy of the generated data. Our proposed approach based on diffusion models exhibits promising performance in few-shot EMG signal generation task. This research contributes to the field of EMG signal synthesis and offers new possibilities for data augmentation, enabling more robust and accurate analysis of EMG signals in various applications.

In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods and overly specific designs, leading to high computational complexity and limited scalability. To address this challenge, this study introduces a deep learning network based on channel feature compression—partial channel selection sEMG net (PCS-EMGNet). This network combines channel feature compression (channel selection) and feature extraction (partial block), aiming to reduce the model’s parameter count while maintaining recognition accuracy. PCS-EMGNet extracts high-dimensional feature vectors from sEMG signals through the partial block, decoding spatial and temporal feature information. Subsequently, channel selection compresses and filters these high-dimensional feature vectors, accurately selecting channel features to reduce the model’s parameter count, thereby decreasing computational complexity and enhancing the model’s processing speed. Moreover, the proposed method ensures the stability of classification, further improving the model’s capability of recognizing features in sEMG signal data. Experimental validation was conducted on five benchmark databases, namely the NinaPro DB4, NinaPro DB5, BioPatRec DB1, BioPatRec DB2, and BioPatRec DB3 datasets. Compared to traditional gesture recognition methods, PCS-EMGNet significantly enhanced recognition accuracy and computational efficiency, broadening its application prospects in real-world settings. The experimental results showed that our model achieved the highest average accuracy of 88.34% across these databases, marking a 9.96% increase in average accuracy compared to models with similar parameter counts. Simultaneously, our model’s parameter size was reduced by an average of 80% compared to previous gesture recognition models, demonstrating the effectiveness of channel feature compression in maintaining recognition accuracy while significantly reducing the parameter count.

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

Estimating joint moment using surface electromyography (sEMG) signals is vital in industrial and rehabilitation area. Current prediction methods mostly focused on extracting single-scale temporal features. However, the spatial information between channels is also essential in joint moment estimation. Therefore, we introduced a trans-scale hybrid (TranSEMG) model that has two streams. The spatial-feature-net module was designed for one stream, which can extract the spatial features according to different effects of muscles corresponding to different channels on joint moment. Another stream used the temporal convolutional network (TCN) module to learn temporal features. Then, we utilized the designed fusion module (FM) for temporal and spatial features fusion. The model was tested on experimental data collected from nine healthy subjects walking on treadmill, level ground, and ramp. The root-mean-square error (RMSE), the coefficient of determination (R2), and variance accounted for (VAF) between the predicted and inverse dynamics moment are used to evaluate the prediction accuracy. Compared with the state-of-the-art models, the trans-scale hybrid model can predict joint moments with lower RMSE, higher R2, and higher VAF values. Furthermore, the model also has good versatility and showed good performance in other walking environments, such as ramp and level ground. In conclusion, this study provided a new model to get more features from sequences to predict the hip joint moment and achieved a good prediction accuracy.