发表机构
Dalian University of Technology; The University of Tokyo; Dalian Medical University; Second Affiliated Hospital of Dalian Medical University（大连理工大学; 东京大学; 大连医科大学; 大连医科大学附属第二医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出以数据为中心的神经运动接口范式，开发无线高带宽系统结合肌肉特异性电极构建表面肌电接口，用2210参数模型实现34种手势94.36%准确率，为灵巧解码建立千参数基准，可部署于边缘设备。
AI 中文摘要
灵巧的人机交互需要直观且表达力强的接口，可高效部署在受约束的边缘设备上。基于柔性材料的神经运动接口具有巨大潜力，因为它们能将人类运动意图解码为自然控制。尽管新兴的柔性电子皮肤可实现可穿戴高保真数据采集，但实际部署不可避免地存在计算资源与便携性之间的权衡。我们提出一种以数据为中心的范式，其中生理特征具有基本可分性，为识别提供足够的判别线索。开发的用于采集各种电生理信号的无线高带宽系统，与肌肉特异性电极集成后，形成基于表面肌电图的接口。利用高度可分的数据，一个2210参数的模型在34种手势上达到94.36%的准确率，且可快速部署在边缘设备上，为灵巧解码建立了新的千参数基准。进一步阐明了以数据为中心范式中潜在的数据-算法交互，证明其在现实场景中的可行性。本研究为可靠神经运动接口的实际部署提供了有原则且经过验证的路径。
英文摘要
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.
