Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures and the entanglement of forearm muscles make accurate recognition intrinsically challenging. Existing approaches typically reduce task complexity by relying on classification-based machine learning, limiting the controllable degrees of freedom and compromising on natural interaction. We present an end-to-end framework for continuous EMG-to-kinematics regression using only consumer-grade hardware. The framework combines an 8-channel EMG armband, a single webcam, and an automatic synchronization procedure, enabling the collection of the EMG Finger-Kinematics dataset (EMG-FK), a 10-h dataset of synchronized EMG and 15 finger joint angles from 20 participants performing rich, unconstrained right-hand motions. We also introduce the Temporal Riemannian Regressor (TRR), a lightweight GRU-based model that uses sequences of multi-band Riemannian covariance features to decode finger motion. Across EMG-FK and the public emg2pose benchmark, TRR outperforms state-of-the-art methods in both intra- and cross-subject evaluation. On EMG-FK, it reaches an average absolute error of $9.79 °\pm 1.48$ in intra-subject and $16.71 °\pm 3.97$ in cross-subject. Finally, we demonstrate real-time deployment on a Raspberry Pi 5 and intuitive control of a robotic hand; TRR runs at nearly 10 predictions/s and is roughly an order of magnitude faster than state-of-the-art approaches. Together, these contributions lower the barrier to reproducible, real-time EMG-based decoding of high-dimensional finger motion, and pave the way toward more natural and intuitive control of embedded EMG-based systems.

翻译：从表面肌电信号（EMG）中连续估计高维手指运动学参数，能够为手部假肢、AR/XR界面及远程操作提供自然控制。然而，人类手势的复杂性和前臂肌肉的混叠效应使准确识别面临本质性挑战。现有方法通常通过基于分类的机器学习降低任务复杂度，这不仅限制了可控自由度，也牺牲了交互的自然性。我们提出仅使用消费级硬件的端到端连续EMG-运动学回归框架。该框架融合8通道EMG臂环、单摄像头及自动同步流程，构建了EMG手指运动学数据集（EMG-FK），包含20名受试者在丰富无约束右手运动状态下同步采集的10小时EMG与15个手指关节角度数据。同时提出时间黎曼回归器（TRR），这是一种轻量级GRU模型，通过多频段黎曼协方差特征序列解码手指运动。在EMG-FK与公开基准数据集emg2pose上，TRR在受试者内与跨受试者评估中均超越当前最优方法。在EMG-FK上，受试者内平均绝对误差为$9.79 °\pm 1.48$，跨受试者为$16.71 °\pm 3.97$。最后，我们在树莓派5上实现实时部署并完成机械手直观控制；TRR以每秒近10次预测的速度运行，比现有最优方法快约一个数量级。这些成果共同降低了可复现、实时高维手指运动EMG解码的门槛，为更自然直观的嵌入式EMG系统控制铺平道路。