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.

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.

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.

Microarrays play a crucial role in detecting diverse biological assays, supported by high-throughput fluorescent scanners that stem from laser confocal scanning mechanisms or CCD wide-field imaging technology. Nonetheless, each scanner exhibits significant limitations. To address these drawbacks, this study proposes a novel design for detecting Cy3 fluorescence microarrays using a cooled CCD and high-power LED, which demonstrates excellent performance to overcome the high complexity of the laser confocal technique and the low efficiency of high-pressure gas lamps in traditional CCD scanner by ExviewHAD CCD of ICX694AL and single-core powerful LED of PT54G. The experimental results, including comparative testing of MGMED BAC microarrays using laser confocal scanning and cooled CCD reading, reveal that the novel CCD scanner achieves a detection sensitivity of 1.12 fluor/μm ² , a reading resolution of 10.0μm, and a measuring repeatability indicator R_v ≤ 2.33%. Additionally, to mitigate no-linear response in quantitative detection of Cy3 fluorophores, the calibration curve is analyzed by the least squares approximation (LSA), and the LSA fitting of N=3 in the CCD linearity region yields a maximum relative error of only 2.35%. These exceptional performance metrics indicate that this newly developed CCD wide-field scanner integrated with a high-power LED, exhibits excellent capability in detecting Cy3 microarrays.

With the rapid development of the power industry, the number of power cables has increased significantly, making the improvement of maintenance efficiency a critical issue. Reducing accidents caused by the manual handling of heavy electrical cover plates has become a pressing concern. However, the maintenance of power cables often requires precise positioning and efficient movement of these heavy cover plates in energy-poor environments. Existing object detection models typically not only require high-performance computing but also have with high cost and large volume, rendering them quite unsuitable for deployment in the resource-constrained system. To address these challenges, we propose an automated solution that integrates negative pressure adsorption and computer vision techniques. The core module of this method is Cover Plates Edge Line-YOLO (CPEL-YOLO), a lightweight model based on You Only Look Once (YOLO). In CPEL-YOLO, it leverages MobileNetV3 with efficient multiscale attention (EMA) and C3-Faster with EMA to focus the model on precise edge lines with reduced computational complexity. Additionally, Scylla-IoU (SIoU) is incorporated to optimize the training process. A dataset of 1009 cover plate images was constructed for training and evaluation. Experimental results show that CPEL-YOLO achieves a mAP@0.5 of 88.1%, comparable to YOLOv5s, while reducing model size by 42% and improving precision by 4.4%.

the polymerase chain reaction (PCR) is widely used for amplifying the bio-sequence in DNA quantitative detection to overcome the limited sensitivity of biochip scanners, but the PCR is not suitable for medical on-site testing due to its complex, expensive and bulky heating and cooling equipment. This study proposes a new approach based on cyclic cleaving by DNA walkers, which exhibits a proportional relationship between the reaction product and the initial concentration of DNA walker when the detecting probe sequence is excessive. With that, the fluorescence probe of Cy3-DRNA/MCH/Au is constructed and bonded on the Au-surface of Indium Tin Oxide (ITO) glass. Via the CCD sensor, the grayscale intensity of each pixel is linearly converted to correspond to the Cy3 concentration on the ITO-Au surface of the detection probe. And the reaction products cleaved by DNA walker are proportional to the accumulation of all intensity differences for each pixel in the probe. Experimental results show that the biochemical cleavage reaction maintains good linear characteristics with the reaction time from 0 to 40 min, and the linear fitting correlation coefficient R2 is 0.99147 at the biochemistry cleaving time of 25 min. This new approach provides a novel and promising technique for biological signal amplification and quantitative detection based on DNA walker, which is expected to have a wide range of applications in biology, medicine, food, and the environment.