MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects

• URL: http://arxiv.org/abs/2609.17194v1
• Date: Tue, 15 Sep 2026 13:51:13 GMT
• Title: MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects
• Abstract summary: MyoFlow is the first discriminative flow-matching framework for HD-sEMG recognition across sessions and subjects.<n>It recasts classification as anchor-tied transport, moving encoded windows toward gesture anchors that serve as transport targets.<n>MyoFlow achieves 91.71% mean zero-shot accuracy and 97.39% mean few-shot accuracy across multiple days on the CEMHSEY dataset.
• Score: 6.08900888109429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-density surface electromyography (HD-sEMG) gesture recognition supports prosthetic control, assistive robotics, and rehabilitation, but electrode re-donning and physiological variability cause distribution shifts that degrade accuracy across sessions and subjects. Generative HD-sEMG models primarily synthesize signals for augmentation; although diffusion models enhance representation learning, prediction still relies on a separate classifier. To tie learned dynamics to the decision rule, we propose MyoFlow, the first discriminative flow-matching framework for HD-sEMG recognition across sessions and subjects. It recasts classification as anchor-tied transport: a domain-conditioned rectified flow moves encoded windows toward gesture anchors that serve as transport targets and define the nearest-anchor decision geometry, enabling zero-shot prediction without an independent head. On the Hyser dataset, MyoFlow improves mean cross-session and cross-subject accuracy over the strongest diffusion-based baseline by 4.24\% and 6.37\%, respectively, and achieves 91.71\% mean zero-shot accuracy and 97.39\% mean few-shot accuracy across multiple days on the CEMHSEY dataset.

Related papers

• Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation [71.74203645336385]
  Impute-EM alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data.<n>We characterize the update and show that the observed mask-indexed marginals match the targets at the limit.
  arXiv  Detail & Related papers  (2026-09-14T09:39:13Z)
• Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization [5.20374411247051]
  Harmonization methods aim to remove site-induced shifts while preserving biologically meaningful signal.<n>We propose the Feynman Kac Reweighted Schrodinger Bridge Matching model to address this problem.<n>We evaluate the method on tau SUVR maps, harmonizing PI-2620 data from the HABS-ADNI cohort into the AV-1451 domain.
  arXiv  Detail & Related papers  (2026-06-16T02:06:36Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals [0.8594140167290097]
  We propose a hybrid transformer model that processes continuous ECG signals alongside seven heart rate variability (HRV) features.<n>We employ representation learning techniques, including Maximum Mean Discrepancy (MMD), to align feature distributions between source and target domains.<n>When tested on wearable device data with an unseen domain, the model achieved an F1-macro 95% and balanced accuracy of 96.15%.
  arXiv  Detail & Related papers  (2026-05-05T22:03:48Z)
• Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer's Disease Prediction [0.0]
  We propose a Transformer-based diagnostic framework that combines synthetic data generation with graph representation learning and transfer learning.<n>A class-conditional denoising diffusion probabilistic model (DDPM) is trained on the real-world NACC dataset to generate a large synthetic cohort.<n> Modality-specific Graph Transformer encoders are first pretrained on this synthetic data to learn robust, class-discriminative representations.
  arXiv  Detail & Related papers  (2025-11-24T19:34:53Z)
• Authentic Discrete Diffusion Model [72.31371542619121]
  Authentic Discrete Diffusion (ADD) framework redefines prior pseudo-discrete approaches.<n>ADD reformulates the diffusion input by directly using float-encoded one-hot class data.<n> experiments demonstrate that ADD achieves superior performance on classification tasks compared to the baseline.
  arXiv  Detail & Related papers  (2025-10-01T15:51:10Z)
• Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation [11.375625987308927]
  Prior-Guided Residual Diffusion (PGRD) is a diffusion-based framework that learns voxel-wise distributions.<n> evaluated on representative MRI and CT datasets.
  arXiv  Detail & Related papers  (2025-09-01T10:13:15Z)
• FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator [0.0]
  Synthetic electrocardiogram generation serves medical AI applications requiring privacy-preserving data sharing and training dataset augmentation.<n>Current diffusion-based methods achieve high generation quality but require hundreds of neural network evaluations during sampling.<n>We propose FlowECG, a flow matching approach that adapts the SSSD-ECG architecture by replacing the iterative diffusion process with continuous flow dynamics.
  arXiv  Detail & Related papers  (2025-08-31T09:19:22Z)
• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions [64.17845687013434]
  We propose ReDiSC, a structured diffusion model for structured node classification.<n>We show that ReDiSC achieves superior or highly competitive performance compared to state-of-the-art GNN, label propagation, and diffusion-based baselines.<n> Notably, ReDiSC scales effectively to large-scale datasets on which previous structured diffusion methods fail due to computational constraints.
  arXiv  Detail & Related papers  (2025-07-19T04:46:53Z)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.