用生理数据驱动的神经接口，让小设备也能精准识别34种手势。
Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
- 基于肌电数据的可穿戴接口，通过高可分性信号提升识别效率。
- 仅2210参数模型即达94.36%准确率，适合边缘设备快速部署。
- 为便携式人机交互提供可验证的实用化路径，适合可穿戴开发。
灵巧的人机交互需要直观且高效的接口，可在资源受限的边缘设备上部署。基于柔性材料的神经运动接口具有潜力，能将人体运动意图转化为自然控制信号。尽管新兴的柔性电子皮肤实现了可穿戴的高保真数据采集，但实际部署仍需在计算资源与便携性之间权衡。本文提出一种以数据为中心的范式，利用生理特征的固有可分性，提供充分的判别线索用于识别。构建了一个无线、高带宽系统，用于采集多种电生理信号，结合肌群特异性电极，形成基于表面肌电的接口。利用高度可分的数据，一个仅含2,210个参数的模型在34种手势上达到94.36%的准确率，可快速部署于边缘设备，建立了千参数量级下灵巧解码的新基准。进一步揭示了该范式中数据与算法的交互机制，证明其在真实场景中的可行性。本研究为可靠神经运动接口的实际部署提供了原理性且经过验证的路径。
原文摘要 · Abstract (English)
Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter benchmark for dexterous decoding. The underlying data-algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.
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