Daoyong Peng

Hierarchical Channel-Aware Optimization Graph Convolution With Contrastive Learning for Emotion Recognition From Body Motion

Yuehua Chen, Zhelong Wang, Daoyong Peng, Zhao H et autres

Graph Convolutional Networks (GCNs) have shown strong potential in skeleton-based emotion recognition. However, existing methods often struggle to capture fine-grained details of key body parts. A Hierarchical Channel-Aware Optimization Graph Convolutional Network (HCO-GCN) is proposed. This framework enhances emotion recognition by integrating …

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Fine-Grained Assessment of Upper-Limb Bradykinesia Through Multimodal Feature Enhancement and Deep Learning

Fang Lin, Zhelong Wang, Zhenglin Li, Hongyu Zhao et autres

Bradykinesia is a hallmark symptom of Parkinson’s disease (PD) that significantly affects patients’ functional abilities and quality of life. This study proposed a fine-grained classification method for evaluating the level of bradykinesia in PD patients. Based on inertial signals, surface electromyographic (sEMG) …

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ST-DECNet: A Spatiotemporal Graph Learning Framework for Epileptic Seizure Prediction via Dynamic Effective Connectivity in EEGs

Hongkai Zhao, Zhelong Wang, Daoyong Peng, Hongyu Zhao et autres

Effective connectivity represents causal interactions in the brain, yet analyzing its nonlinear and dynamic properties in electroencephalogram (EEG) data, which is measured by recording electrical activity through scalp electrodes, remains a significant challenge. This work introduces a novel framework, spatio-temporal dynamic effective …

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Effect of hemiplegia on gait symmetry, complexity and variability during lower limb movements

Hongyu Zhao, Ronghua Shi, Zhelong Wang, Sen Qiu et autres

The effect of hemiplegia on the kinematic characteristics of the hip, knee, and ankle joints during the gait cycle was explored in detail in this study by comparing and analyzing the changes in the lower limb joint angles in hemiplegic patients with …

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A comprehensive evaluation method for frailty based on semi-supervised learning and transfer-learning

Jiaxi Li, Zhelong Wang, Sen Qiu, Daoyong Peng et autres

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A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

Xiaoshuang Ru, Shi-Long Zhao, Weidao Chen, Jiangfen Wu et autres

BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a …

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Cell-therapy for Parkinson’s disease: a systematic review and meta-analysis

Fang Wang, Zhengwu Sun, Daoyong Peng, Shikha Gianchandani et autres

BACKGROUND: Cell-based strategies focusing on replacement or protection of dopaminergic neurons have been considered as a potential approach to treat Parkinson's disease (PD) for decades. However, despite promising preclinical results, clinical trials on cell-therapy for PD reported mixed outcomes and a thorough …

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Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography

Hongyu Zhao, Zhibo Qiu, Daoyong Peng, Fang Wang et autres

Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body's movement intention. In this paper, the OpenSim software is …

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Analysis and evaluation of hemiplegic gait based on wearable sensor network

Hongyu Zhao, Haiyang Xu, Zhelong Wang, Litong Wang et autres

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