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Zijun Wei

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7 papers

2 author rows

JBHI Journal 2026 Journal Article

A Conditional GAN-based Framework for Sparse sEMG Data Augmentation with Muscle Synergy Prior Constraints

Meiju Li
Zijun Wei
Zhi-Qiang Zhang
Bin Yang
Sheng Quan Xie

The scarcity of high-quality surface electromyography (sEMG) data, caused by ethical constraints, privacy concerns, and noise interference, poses significant challenges for developing robust deep learning models in sEMG analysis. Multi-channel sEMG signals exhibit complex inter-channel correlations reflecting neuromuscular coordination. However, existing generative methods suffer from error accumulation in sequential channel generation, insufficient inter-channel relationship modeling, and lack of physiological constraints, producing data-driven valid but physiologically implausible signals that compromise biological fidelity for clinical applications. To address these fundamental limitations, we propose a Muscle Synergy-Constrained Conditional GAN (MS-cGAN) framework that simultaneously generates multi-channel sEMG signals while preserving bio-mechanical fidelity. Firstly, A novel Graph Convolutional Network (GCN)-based generator architecture specifically tailored for sparse sEMG signals, which enables the generator to capture and model complex inter-channel relationship features through graph-based representation learning, thereby circumventing error accumulation issues by leveraging the inherent inter-channel correlations. Secondly, Integration of Muscle Synergy (MS) prior constraints as dynamic loss functions based on MS theory, which enforces generator optimization within a physiologically plausible parameter space and ensures signals maintain synergistic consistency with underlying physiological mechanisms. Lastly, experiments on IRASS datasets and public datasets (NinaPro DB1 and DB2) demonstrate that MS-cGAN significantly improves signal authenticity and enhances downstream task performance compared to traditional GANs and state-of-the-art diffusion models. The generated data effectively supplement scarce sEMG datasets and improve kinematic prediction precision for deep learning models.

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JBHI Journal 2026 Journal Article

A Transformer Framework Informed by Muscle Anatomy and Sequence-to-Sequence Translation for Continuous Joint Kinematics Prediction Using sEMG

Zijun Wei
Zhiqiang Zhang
Sheng Quan Xie

The key to achieving assist-as-needed (AAN) control in rehabilitation robots lies in accurately predicting patient motion intentions. This study, for the first time, redefines motion intention prediction from the perspective of sequence-to-sequence translation by analogizing sEMG signals and joint angles to the source language and target language, respectively. The proposed 3DCNN-TF model achieves precise translation of neural control signals into kinematic representations. This model comprises three modules: an sEMG “sentence” generation module that compiles multiple sEMG sliding windows into a “sentence, ” a 3DCNN module based on muscle anatomy and electrode placement to extract muscle synergy features from each “word” in the “sentence, ” and a Transformer (TF) module that autoregressively generates the next joint angle as the translation result. Experimental results indicate that the 3DCNN-TF model achieves superior overall performance compared to eight baseline models and existing studies in continuously predicting wrist and knee flexion/extension angles across varying speeds. Moreover, the 3DCNN-TF achieves an optimal balance between prediction accuracy and computational efficiency while exhibiting exceptional robustness and generalizability. Specifically, the 3DCNN-TF achieves average nRMSE and R 2 values of (6. 2% /95. 5% ) and (5. 5% /96. 2% ) on wrist and knee datasets, respectively, with an average training time of less than two minutes. Additionally, the 3DCNN-TF can predict joint angles up to 300 ms in advance without compromising accuracy, which is critical for real-time AAN control in rehabilitation robots.

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JBHI Journal 2025 Journal Article

Continuous Prediction of Wrist Joint Kinematics Using Surface Electromyography From the Perspective of Muscle Anatomy and Muscle Synergy Feature Extraction

Zijun Wei
Meiju Li
Zhi-Qiang Zhang
Sheng Quan Xie

Post-stroke upper limb dysfunction severely impacts patients' daily life quality. Utilizing sEMG signals to predict patients' motion intentions enables more effective rehabilitation by precisely adjusting the assistance level of rehabilitation robots. Employing the muscle synergy (MS) features can establish more accurate and robust mappings between sEMG and motion intentions. However, traditional matrix factorization algorithms based on blind source separation still exhibit certain limitations in extracting MS features. This paper proposes four deep learning models to extract MS features from four distinct perspectives: spatiotemporal convolutional kernels, compression and reconstruction of sEMG, graph topological structure, and the anatomy of target muscles. Among these models, the one based on 3DCNN predicts motion intentions from the muscle anatomy perspective for the first time. It reconstructs 1D sEMG samples collected at each time point into 2D sEMG frames based on the anatomical distribution of target muscles and sEMG electrode placement. These 2D frames are then stacked as video segments and input into 3DCNN for MS feature extraction. Experimental results on both our wrist motion dataset and public Ninapro DB2 dataset demonstrate that the proposed 3DCNN model outperforms other models in terms of prediction accuracy, robustness, training efficiency, and MS feature extraction for continuous prediction of wrist flexion/extension angles. Specifically, the average nRMSE and R 2 values of 3DCNN on these two datasets are (0. 14/0. 93) and (0. 04/0. 95), respectively. Furthermore, compared to existing studies, the 3DCNN outperforms musculoskeletal models based on direct collocation optimization, physics-informed GANs, and CNN-LSTM-based deep Kalman filter models when evaluated on our dataset.

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NeurIPS Conference 2024 Conference Paper

Uncertainty-aware Fine-tuning of Segmentation Foundation Models

Kangning Liu
Brian Price
Jason Kuen
Yifei Fan
Zijun Wei
Luis Figueroa
Krzysztof J. Geras
Carlos Fernandez-Granda

The Segment Anything Model (SAM) is a large-scale foundation model that has revolutionized segmentation methodology. Despite its impressive generalization ability, the segmentation accuracy of SAM on images with intricate structures is often unsatisfactory. Recent works have proposed lightweight fine-tuning using high-quality annotated data to improve accuracy on such images. However, here we provide extensive empirical evidence that this strategy leads to forgetting how to "segment anything": these models lose the original generalization abilities of SAM, in the sense that they perform worse for segmentation tasks not represented in the annotated fine-tuning set. To improve performance without forgetting, we introduce a novel framework that combines high-quality annotated data with a large unlabeled dataset. The framework relies on two methodological innovations. First, we quantify the uncertainty in the SAM pseudo labels associated with the unlabeled data and leverage it to perform uncertainty-aware fine-tuning. Second, we encode the type of segmentation task associated with each training example using a $\textit{task prompt}$ to reduce ambiguity. We evaluated the proposed Segmentation with Uncertainty Model (SUM) on a diverse test set consisting of 14 public benchmarks, where it achieves state-of-the-art results. Notably, our method consistently surpasses SAM by 3-6 points in mean IoU and 4-7 in mean boundary IoU across point-prompt interactive segmentation rounds. Code is available at https: //github. com/Kangningthu/SUM

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ICLR Conference 2023 Conference Paper

Interactive Portrait Harmonization

Jeya Maria Jose Valanarasu
He Zhang 0004
Jianming Zhang 0001
Yilin Wang 0002
Zhe Lin 0001
Jose Echevarria
Yinglan Ma
Zijun Wei

Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected region in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits.

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NeurIPS Conference 2018 Conference Paper

Sequence-to-Segment Networks for Segment Detection

Zijun Wei
Boyu Wang
Minh Hoai Nguyen
Jianming Zhang
Zhe Lin
Xiaohui Shen
Radomir Mech
Dimitris Samaras

Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments. To address this problem, we propose the Sequence-to-Segment Network (S$^2$N), a novel end-to-end sequential encoder-decoder architecture. S$^2$N first encodes the input into a sequence of hidden states that progressively capture both local and holistic information. It then employs a novel decoding architecture, called Segment Detection Unit (SDU), that integrates the decoder state and encoder hidden states to detect segments sequentially. During training, we formulate the assignment of predicted segments to ground truth as bipartite matching and use the Earth Mover's Distance to calculate the localization errors. We experiment with S$^2$N on temporal action proposal generation and video summarization and show that S$^2$N achieves state-of-the-art performance on both tasks.

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NeurIPS Conference 2016 Conference Paper

Learned Region Sparsity and Diversity Also Predicts Visual Attention

Zijun Wei
Hossein Adeli
Minh Hoai Nguyen
Greg Zelinsky
Dimitris Samaras

Learned region sparsity has achieved state-of-the-art performance in classification tasks by exploiting and integrating a sparse set of local information into global decisions. The underlying mechanism resembles how people sample information from an image with their eye movements when making similar decisions. In this paper we incorporate the biologically plausible mechanism of Inhibition of Return into the learned region sparsity model, thereby imposing diversity on the selected regions. We investigate how these mechanisms of sparsity and diversity relate to visual attention by testing our model on three different types of visual search tasks. We report state-of-the-art results in predicting the locations of human gaze fixations, even though our model is trained only on image-level labels without object location annotations. Notably, the classification performance of the extended model remains the same as the original. This work suggests a new computational perspective on visual attention mechanisms and shows how the inclusion of attention-based mechanisms can improve computer vision techniques.

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