FatigueFormer: Static-Temporal Feature Fusion for Robust sEMG-Based Muscle Fatigue Recognition

• URL: http://arxiv.org/abs/2603.26841v1
• Date: Fri, 27 Mar 2026 08:48:31 GMT
• Title: FatigueFormer: Static-Temporal Feature Fusion for Robust sEMG-Based Muscle Fatigue Recognition
• Abstract summary: We present FatigueFormer, a semi-end-to-end framework that learns interpretable muscle fatigue dynamics from surface electromyography (sEMG)<n>FatigueFormer employs parallel Transformer-based sequence encoders to separately capture static and temporal feature dynamics.<n>It achieves state-of-the-art accuracy and strong generalization under mild-fatigue conditions.
• Score: 11.736046353721447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present FatigueFormer, a semi-end-to-end framework that deliberately combines saliency-guided feature separation with deep temporal modeling to learn interpretable and generalizable muscle fatigue dynamics from surface electromyography (sEMG). Unlike prior approaches that struggle to maintain robustness across varying Maximum Voluntary Contraction (MVC) levels due to signal variability and low SNR, FatigueFormer employs parallel Transformer-based sequence encoders to separately capture static and temporal feature dynamics, fusing their complementary representations to improve performance stability across low- and high-MVC conditions. Evaluated on a self-collected dataset spanning 30 participants across four MVC levels (20-80%), it achieves state-of-the-art accuracy and strong generalization under mild-fatigue conditions. Beyond performance, FatigueFormer enables attention-based visualization of fatigue dynamics, revealing how feature groups and time windows contribute differently across varying MVC levels, offering interpretable insight into fatigue progression.

Related papers

• TTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs [83.78708832901194]
  Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild.<n>However, real-world deployment remains challenging: modalities may be missing or noisy at test time.<n>We propose a solution in which a frozen teacher guides an adaptive low-rank student via self-distillation.<n>Experiments on MELD, DFEW, and BAH under 0%-50% missing modalities show that this unified adaptation-restoration design consistently outperforms entropy-based adaptation, RAG, and perplexity-based generation.
  arXiv  Detail & Related papers  (2026-08-18T23:40:28Z)
• Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Chassis Adaptation [49.23697454856427]
  World model (WM)-based reinforcement learning enables sample-efficient end-to-end autonomous driving learning.<n>Most WMs operate on bird's-eye-view (BEV) representations that are inherently egocentric.<n>This work proposes DynaDreamer, a dynamics-augmented Dreamer-style reinforcement learning method.
  arXiv  Detail & Related papers  (2026-07-15T03:20:53Z)
• Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection [6.801129260713164]
  We propose an energy-efficient framework for muscle fatigue detection based on Spiking Neural Networks (SNNs)<n>We introduce a quantization-compatible robustness training scheme (SDH) that combines multiple regularization terms to improve computation under noisy conditions.<n>Results demonstrate the framework's strong potential for real-time deployment in low-power wearable systems.
  arXiv  Detail & Related papers  (2026-07-13T04:05:13Z)
• SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics [49.771521708359955]
  SurgVista is a surgical world model that mitigates spatial interaction incoherence and temporal fidelity collapse.<n>It consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity.
  arXiv  Detail & Related papers  (2026-06-18T07:47:28Z)
• FatigueFusion: Latent Space Fusion for Fatigue-Driven Motion Synthesis [3.5195917252906272]
  We present FatigueFusion, a deep-learning architecture for the fusion of fatigue features within a latent representation space.<n>Our framework incorporates algorithmic and data-driven modules to impose subject-specific temporal and spatial fatigue features on nonfatigued motions.<n>FatigueFusion offers an end-to-end architecture that operates directly on non-fatigued joint angle sequences and control parameters.
  arXiv  Detail & Related papers  (2026-04-11T13:12:20Z)
• VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models [64.56065206447788]
  Vision-language models (VLMs) achieve strong performance on standard, high-quality datasets.<n>We present VLM-RobustBench, a benchmark spanning 49 augmentation types across noise, blur, weather, digital, and geometrics.<n>We show that low-severity spatial perturbations often degrade performance more than visually severe photometric corruptions.
  arXiv  Detail & Related papers  (2026-03-06T10:58:02Z)
• All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations [102.94052335735326]
  All-in-one image restoration aims to recover clean images from diverse unknown degradations using a single model.<n>Existing approaches primarily focus on frame-wise degradation variation, overlooking the temporal continuity that naturally exists in real-world degradation processes.<n>We introduce the Smoothly Evolving Unknown Degradations (SEUD) scenario, where both the active degradation set and degradation intensity change continuously over time.
  arXiv  Detail & Related papers  (2026-01-02T02:20:57Z)
• Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement [89.99237142387655]
  We introduce LH-VAE, which enhances semantic robustness through visual semantic constraints and progressive degradations.<n>Latent Harmony is a two-stage framework that redefines VAEs for UHD restoration by jointly regularizing the latent space and enforcing high-frequency-aware reconstruction.<n>Experiments show Latent Harmony achieves state-of-the-art performance across UHD and standard-resolution tasks, effectively balancing efficiency, perceptual quality, and reconstruction accuracy.
  arXiv  Detail & Related papers  (2025-10-09T08:54:26Z)
• Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis [2.0308338182844263]
  Fatigue modeling is essential for motion synthesis tasks to model human motions under fatigued conditions.<n>We present Fatigue-PINN, a deep learning framework based on Physics-Informed Neural Networks.<n>We simulate the fatigue-induced fluctuations in the maximum exerted joint torques by leveraging a PINN adaptation of the Three-Compartment Controller model.
  arXiv  Detail & Related papers  (2025-02-26T11:14:48Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions [9.018775341716305]
  We develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring realtime mental fatigue. The RVT model achieved the highest balanced accuracy(78%) and precision (0.82) compared to the state-of-the-art binary models.
  arXiv  Detail & Related papers  (2023-04-24T21:58:14Z)
• Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study on Walking [6.7733453364081635]
  assessing fatigue of multi-muscle coordination-temporal daily exercises requires neuromuscular features that represent the fatigue-induced characteristics of adaptations of multiple muscles.<n>We propose to depict fatigue by features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model.
  arXiv  Detail & Related papers  (2023-03-30T02:54:59Z)

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.