用跨模态对比学习从肌电图解码手部运动，无需推理时的运动数据。
KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning
- 双编码器联合训练肌电与关节角度，学习几何结构化的嵌入表示。
- 在NinaPro DB8上对拇指活动度解码提升显著，优于多种基线方法。
- 适合假肢控制和康复应用，为可穿戴生物信号建模提供新思路。
从表面肌电图（sEMG）解码手部运动学是可穿戴生物信号处理的核心挑战，具有假肢控制与运动康复的临床意义。现有大多数代表学习方法聚焦于离散手势分类，而较少关注连续回归任务。本文提出KinEMbed，一种用于手部运动学回归的跨模态对比学习框架，通过双编码器联合训练：一个处理窗口化肌电特征，另一个处理运动学目标（关节角度）。所得嵌入保留了运动空间的几何结构，且在推理阶段无需运动信号。在包含11名受试者（含肢体差异者）的NinaPro DB8数据集上，该方法在留出会话上的表现超越主成分分析（PCA）、偏最小二乘法（PLS）、自编码器及对比学习基线（CEBRA），尤其在最困难的拇指活动度解码任务中提升最大。本工作标志着首次将对比学习应用于结构化可穿戴生物信号的手部运动学回归。
原文摘要 · Abstract (English)
Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation. Most representation learning approaches for EMG focus on discrete gesture classification, and few focus on continuous regression. We present KinEMbed, a cross-modal contrastive learning framework for hand kinematics regression that jointly trains dual encoders -- one for windowed EMG features and one for kinematic (joint angle) targets. The resulting embeddings inherit the geometric structure of the kinematic space without requiring kinematic signals at inference time. Evaluating on the NinaPro DB8 dataset that includes both able-bodied users and subjects with limb difference (N=11), KinEMbed outperforms PCA, PLS, autoencoder and contrastive (CEBRA) baselines on held-out sessions, with largest gains on the most challenging thumb degrees of articulation. We position this work as a first step toward contrastive representation learning for regression of hand kinematics from structured wearable biosignals.
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