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<p>Efficient compression of multimodal biosignals is critical for bandwidth-constrained wearable devices. Existing methods compress surface electromyography (sEMG) and accelerometer signals independently, ignoring their inherent coupling where muscle activation drives limb kinematics. We propose a cross-modal alignment framework that explicitly aligns the latent representations of these modalities during training through frame-wise correspondence and temporal-dynamics consistency. Experiments on two Ninapro datasets demonstrate significant improvements over state-of-the-art methods across compression ratios from 20 to 90%, achieving up to 28% higher correlation coefficients compared to traditional methods and consistent 1–3 dB SNR gains over deep learning baselines. Downstream gesture classification experiments further confirm that our method preserves task-relevant features, maintaining over 80% accuracy at CR = 40% while baselines require CR<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\ge\)</EquationSource></InlineEquation>50% for comparable performance. Inference-time latent analysis reveals that alignment training reduces cross-modal drift by 59–64%, validating that the learned consistency persists after the alignment module is removed. Notably, our alignment module serves purely as a training-time regularizer and is completely removed during inference, ensuring that the deployed encoder-decoder system incurs zero additional computational overhead compared to standard single-modality autoencoders. Studies on five backbone architectures confirm that the proposed alignment framework is backbone-agnostic and broadly applicable. Constraint-aware projections onto representative embedded platforms confirm feasibility for real-time wearable deployment.</p>

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Cross-modal latent alignment enables efficient compression of wearable sEMG and accelerometer signals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Yongfei Liu,
  • Chengwen Liu,
  • Ruili Chang,
  • Zhiyou Zhang,
  • Yinghao Zhang

摘要

Efficient compression of multimodal biosignals is critical for bandwidth-constrained wearable devices. Existing methods compress surface electromyography (sEMG) and accelerometer signals independently, ignoring their inherent coupling where muscle activation drives limb kinematics. We propose a cross-modal alignment framework that explicitly aligns the latent representations of these modalities during training through frame-wise correspondence and temporal-dynamics consistency. Experiments on two Ninapro datasets demonstrate significant improvements over state-of-the-art methods across compression ratios from 20 to 90%, achieving up to 28% higher correlation coefficients compared to traditional methods and consistent 1–3 dB SNR gains over deep learning baselines. Downstream gesture classification experiments further confirm that our method preserves task-relevant features, maintaining over 80% accuracy at CR = 40% while baselines require CR\(\ge\)50% for comparable performance. Inference-time latent analysis reveals that alignment training reduces cross-modal drift by 59–64%, validating that the learned consistency persists after the alignment module is removed. Notably, our alignment module serves purely as a training-time regularizer and is completely removed during inference, ensuring that the deployed encoder-decoder system incurs zero additional computational overhead compared to standard single-modality autoencoders. Studies on five backbone architectures confirm that the proposed alignment framework is backbone-agnostic and broadly applicable. Constraint-aware projections onto representative embedded platforms confirm feasibility for real-time wearable deployment.