arXiv：cs.LG· He Wang, Hongyuan Qi, Zhaoxian Zhang, Jinbin Luo, Linyi He, Mehul Motani, Changsheng Wu·· 4 小时前AI 评分36
Neuromotor Hierarchy Network：面向 sEMG 解码鲁棒泛化的生理归纳偏置
Neuromotor Hierarchy Network: Physiological Inductive Biases for Robust Generalization in sEMG Decoding
AI 导读
研究者提出 Neuromotor Hierarchy Network（NHN），通过层级结构从任务监督中学习紧凑的潜在神经运动状态，用于 sEMG 解码。
正文
Abstract:Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interaction. Population-scale decoding remains difficult because the relationship between sEMG and neuromuscular activity varies across users and sessions, while task-relevant dynamics span channels and multiple timescales. Learning waveform-to-output mappings from task labels leaves the distinction between recording variability and coordinated motor activity implicit. We introduce the Neuromotor Hierarchy Network (NHN), which learns a compact latent neuromotor state from task supervision to represent task-relevant neuromuscular coordination. NHN constructs this latent state through a hierarchy inspired by neuromotor this http URL adapts recording statistics while preserving relative this http URL spatiotemporal encoder uses parameter-efficient channel interactions and modulates features with multi-timescale history. The resulting features yield candidate activations of learned motor primitives, which are temporally integrated and continuously weighted to form the state. Theoretical analysis characterizes the efficiency, temporal behavior, and optimization of NHN's core mechanisms. We evaluate the architecture for both continuous hand-pose estimation on emg2pose and touch-typing recognition on emg2qwerty. On emg2pose, NHN reduces user-averaged angular error by 0.52% to 2.84% across all three generalization splits in both Regression and Tracking relative to Hadidi et al.'s best task-specific variants, using 48.42% to 48.51% fewer parameters. On emg2qwerty, NHN reduces beam-search character error rate by 19.40% zero-shot and 30.42% after fine-tuning relative to SplashNet-Upscale, using 65.86% fewer parameters. Physiology-guided inference of a latent neuromotor state supports parameter-efficient sEMG decoding.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07713 [cs.LG] |
| (or arXiv:2610.07713v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07713 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: He Wang [view email]
[v1]
Tue, 6 Oct 2026 04:08:41 UTC (6,663 KB)
来源：arXiv：cs.LG · arxiv.org