Electroacupuncture prevents the development or establishment of chronic pain via IL-33/ST2 signaling in hyperalgesic priming model rats.

BACKGROUND: Chronic pain is acomplexhealth issue. Compared to acute pain, which has a protective value, chronic pain is defined as persistent pain after tissue injury. Few clinical advances have been made to prevent the transition from acute to chronic pain. Electroacupuncture (EA), the most common form of acupuncture, is widely used in clinical practice to relieve pain. METHODS: The hyperalgesic priming model, established via a carrageenan injection followed by a prostaglandin E2 injection, was used to investigate the development or establishment of chronic pain. We observed the hyperalgesic effect of EA on rats and investigated the expression p38 mitogen-activated protein kinase, interleukin-33 (IL-33), and its receptor ST2 in astrocytes in the L4-L6 spinal cord dorsal horns (SDHs) after EA. The IL-33/ST2 signaling pathway in SDH is associated with the development of chronic pain. RESULTS: EA can reverse the pain threshold in hyperalgesic priming model rats and regulates the expression of phosphorylated p38, IL-33, and ST2 in astrocytes in the L4-L6 SDHs. We discovered that EA raises the pain threshold. This suggests that EA can prevent the development or establishment of chronic pain by inhibiting IL-33/ST2 signaling in the lower central nervous system. CONCLUSIONS: EA can alleviate the development or establishment of chronic pain by modulating IL-33/ST2 signaling in SDHs. Our findings will help clinicians understand the mechanisms of EA analgesia.

SDE-YOLO: A Novel Method for Blood Cell Detection.

This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies.

Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals.

Bio-signal based hand motion recognition plays a critical role in the tasks of human-machine interaction, such as the natural control of multifunctional prostheses. Although a large number of classification technologies have been taken to improve the motion recognition accuracy, it is still a challenge to achieve acceptable performance for multiple modality input. This study proposes a multi-modality deep forest (MMDF) framework to identify hand motions, in which surface electromyographic signals (sEMG) and acceleration signals (ACC) are fused at the input level. The proposed MMDF framework constitutes of three main stages, sEMG and ACC feature extraction, feature dimension reduction, and a cascade structure deep forest for classification. A public database "Ninapro DB7" is used to evaluate the performance of the proposed framework, and the experimental results show that it can achieve a significantly higher accuracy than that of competitors. Besides, our experimental results also show that MMDF outperforms other traditional classifiers with the input of the single modality of sEMG signals. In sum, this study verifies that ACC signals can be an excellent supplementary for sEMG, and MMDF is a plausible solution to fuse mulit-modality bio-signals for human motion recognition.

Deep Convolutional Generative Adversarial Network-Based EMG Data Enhancement for Hand Motion Classification.

The acquisition of bio-signal from the human body requires a strict experimental setup and ethical approvements, which leads to limited data for the training of classifiers in the era of big data. It will change the situation if synthetic data can be generated based on real data. This article proposes such a kind of multiple channel electromyography (EMG) data enhancement method using a deep convolutional generative adversarial network (DCGAN). The generation procedure is as follows: First, the multiple channels of EMG signals within sliding windows are converted to grayscale images through matrix transformation, normalization, and histogram equalization. Second, the grayscale images of each class are used to train DCGAN so that synthetic grayscale images of each class can be generated with the input of random noises. To evaluate whether the synthetic data own the similarity and diversity with the real data, the classification accuracy index is adopted in this article. A public EMG dataset (that is, ISR Myo-I) for hand motion recognition is used to prove the usability of the proposed method. The experimental results show that adding synthetic data to the training data has little effect on the classification performance, indicating the similarity between real data and synthetic data. Moreover, it is also noted that the average accuracy (five classes) is slightly increased by 1%-2% for support vector machine (SVM) and random forest (RF), respectively, with additional synthetic data for training. Although the improvement is not statistically significant, it implies that the generated data by DCGAN own its new characteristics, and it is possible to enrich the diversity of the training dataset. In addition, cross-validation analysis shows that the synthetic samples have large inter-class distance, reflected by higher cross-validation accuracy of pure synthetic sample classification. Furthermore, this article also demonstrates that histogram equalization can significantly improve the performance of EMG-based hand motion recognition.

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects' data has been created. In this paper, gesture accuracies under different sampling frequencies and channel's number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.

Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition.

Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under a machine-learning scheme to solve this problem. The model utilizes the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, 16 channels of surface electromyographic signals (i.e., main attribute) and continuous fingertip force (i.e., subattribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type and received more than 90% force grand prediction accuracy at any granular level greater than six. Further, sensitivity analysis verified its robustness with respect to different channel combination and interferences. In comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing.

The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ± 1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision ( R2 ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculature-driven artificial hand control and rehabilitation treatment.

Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition.

Due to the rapid development of human-computer interaction, affective computing has attracted more and more attention in recent years. In emotion recognition, Electroencephalogram (EEG) signals are easier to be recorded than other physiological experiments and are not easily camouflaged. Because of the high dimensional nature of EEG data and the diversity of human emotions, it is difficult to extract effective EEG features and recognize the emotion patterns. This paper proposes a multi-feature deep forest (MFDF) model to identify human emotions. The EEG signals are firstly divided into several EEG frequency bands and then extract the power spectral density (PSD) and differential entropy (DE) from each frequency band and the original signal as features. A five-class emotion model is used to mark five emotions, including neutral, angry, sad, happy, and pleasant. With either original features or dimension reduced features as input, the deep forest is constructed to classify the five emotions. These experiments are conducted on a public dataset for emotion analysis using physiological signals (DEAP). The experimental results are compared with traditional classifiers, including K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). The MFDF achieves the average recognition accuracy of 71.05%, which is 3.40%, 8.54%, and 19.53% higher than RF, KNN, and SVM, respectively. Besides, the accuracies with the input of features after dimension reduction and raw EEG signal are only 51.30 and 26.71%, respectively. The result of this study shows that the method can effectively contribute to EEG-based emotion classification tasks.

Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study.

Surface electromyography (sEMG) has dominated upper-limb prosthesis control for decades due to its simplicity and effectiveness [1-3]. However, the inherent variability of EMG signal hinders the flexible and accurate control of advanced multi-functional prosthesis. This study is an attempt to use ultrasonography (US) as an alternative for prosthetic hand control. A type of multi-sensory module, comprising a single-element ultrasound channel and one sEMG bipolar channel, is customised to ensure a fair comparison between these two modalities. Three machine-learning-oriented approaches were adopted to evaluate the performance in motion classification based on datasets captured from a trans-radial amputee. The experimental results demonstrated that the ultrasound outperformed the sEMG in random (98.9% vs 70.4%) and enhanced-trial-wise (74.10% vs 61.83%) cross-validation, but fell behind the sEMG in trial-wise (39.47% vs 58.04%) validation that is the closest comparison to a real life prosthetic control. This study preliminarily implies that 1) A-mode ultrasound signal can be more stable than the sEMG with minimum electrode shift, but more sensitive to external interference than the sEMG; and 2) to maintain high classification accuracy, US approach may require harsher electrode fixing mechanism or advanced on-line calibration approach.

Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy.

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.

Interface Prostheses With Classifier-Feedback-Based User Training.

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.