Quantifying Muscle Co-activation for Impaired Finger Independence in Stroke Survivors.

OBJECTIVE: Hand impairment frequently occurs in individuals following a stroke. There is evidence of abnormal muscle co-activation that contributes to impaired control of finger independence. This study quantitatively analyzed hand muscle co-activation patterns of chronic stroke survivors. Systematically quantifying the degree of muscle co-activation patterns in stroke survivors can help us to better understand the mechanisms behind compromised finger independence and enables a more accurate assessment of hand impairment. METHODS: We analyzed muscle co-activation patterns both macroscopically and microscopically using high-density surface electromyographic (HD-sEMG) signals and decomposed motor unit signals from extrinsic and intrinsic flexor/extensor muscles. The muscle co-activation patterns between both sides of stroke survivors and neurologically intact controls were compared. RESULTS: We observed increased levels of co-activation in the affected sides of stroke survivors compared with their contralateral sides and the control groups, with a higher degree in the extrinsic muscles than the intrinsic muscles. The asymmetry in muscle co-activation between hands correlated with impaired finger force independence and clinical assessment scales. In the micro-level analysis of motor unit action potentials (MUAPs) distributions, we observed a notable increase in action potential spread of MUAPs in the individual affected extrinsic muscles, but the altered MUAP distribution did not correlate with clinical assessment scales. CONCLUSION: We systematically quantified abnormal muscle co-activation patterns in impaired finger independence after stroke. SIGNIFICANCE: With further development, the outcomes provide a comprehensive understanding of hand dexterity deficits in stroke survivors, which may provide guidance for targeted rehabilitation strategies and offer a potential for automated impairment evaluations.

Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network.

With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. The EMG pattern recognition experiments are conducted on HD-sEMG data of 17 gestures from 11 subjects. The ablation experiments show that each functional module of the proposed CNN-MSTGCN network has played a more or less positive role in improving the performance of EMG pattern recognition. The user-independent recognition experiments and the transfer learning-based cross-user recognition experiments verify the advantages of the proposed CNN-MSTGCN network in improving recognition rate and reducing user training burden. In the user-independent recognition experiments, CNN-MSTGCN achieves the recognition rate of 68%, which is significantly better than those obtained by residual network-50 (ResNet50, 47.5%, p < 0.001) and long-short-term-memory (LSTM, 57.1%, p=0.045). In the transfer learning-based cross-user recognition experiments, TL-CMSTGCN achieves an impressive recognition rate of 92.3%, which is significantly superior to both TL-ResNet50 (84.6%, p = 0.003) and TL-LSTM (85.3%, p = 0.008). The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.

Principal component analysis of photoplethysmography signals for improved gesture recognition.

In recent years, researchers have begun to introduce photoplethysmography (PPG) signal into the field of gesture recognition to achieve human-computer interaction on wearable device. Unlike the signals used for traditional neural interface such as electromyography (EMG) and electroencephalograph (EEG), PPG signals are readily available in current commercial wearable devices, which makes it possible to realize practical gesture-based human-computer interaction applications. In the process of gesture execution, the signal collected by PPG sensor usually contains a lot of noise irrelevant to gesture pattern and not conducive to gesture recognition. Toward improving gesture recognition performance based on PPG signals, the main contribution of this study is that it explores the feasibility of using principal component analysis (PCA) decomposition algorithm to separate gesture pattern-related signals from noise, and then proposes a PPG signal processing scheme based on normalization and reconstruction of principal components. For 14 wrist and finger-related gestures, PPG data of three wavelengths of light (green, red, and infrared) are collected from 14 subjects in four motion states (sitting, walking, jogging, and running). The gesture recognition is carried out with Support Vector Machine (SVM) classifier and K-Nearest Neighbor (KNN) classifier. The experimental results verify that PCA decomposition can effectively separate gesture-pattern-related signals from irrelevant noise, and the proposed PCA-based PPG processing scheme can improve the average accuracies of gesture recognition by 2.35∼9.19%. In particular, the improvement is found to be more evident for finger-related (improved by 6.25∼12.13%) than wrist-related gestures (improved by 1.93∼5.25%). This study provides a novel idea for implementing high-precision PPG gesture recognition technology.

The Role of Transcription Factors in Coronary Artery Disease and Myocardial Infarction.

Coronary artery disease (CAD) and its main complication, myocardial infarction (MI), is a complex disease caused by environmental and genetic factors and their interaction. Family-based linkage analysis and genome-wide association studies have indicated many of genetic variations related to CAD and MI in recent years. Some are in the coding sequence, which mediates the coding protein, while others are in the non-coding region, which affects the expression of adjacent genes and forms differential gene expression. These variants and differential expressions will have varying degrees of impact on the development of the cardiovascular system and normal heart electrical activity function, subsequently leading to CAD and MI. Among these affected genes, some Transcription Factors (TFs), as important means of transcriptional regulation, have a key role in the pathogenesis of coronary artery disease and myocardial infarction. The GATAs binding protein 2 (GATA2) enhances monocyte adhesion and promoted vessel wall permeabilization through vascular EC adhesion molecule 1 (VCAM-1) upregulation, further revealing its atherosclerosis-promoting role. Myocyte enhancer factor 2 (MEF2) has a role in fostering many functions of the atherosclerotic endothelium and is a potential therapeutic target for atherosclerosis, thrombosis, and inflammation. Nuclear factor-kappa B (NF-κB) is an important promoter of vascular endothelial growth factor (VEGF)-driven angiogenesis, and its pathway has a key role in atherosclerosis-related complications such as angiogenesis, inflammation, apoptosis, and immune effects. Activating transcription factor 3 (ATF3) may be a novel prognostic biomarker and therapeutic target for atherosclerosis. The important role of signal transducer and activator of transcription 3 (STAT3) (especially in mitochondria) in endothelial cells (EC) dysfunction, inflammation, macrophage polarization and immunity in atherosclerosis.