用于零样本肌电运动分类的原型适配
Prototype Adaptation for Zero-Shot sEMG Movement Classification
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中文总结 AI 辅助
研究针对上肢截肢者假肢控制中组合运动识别难题，提出CPI和SAP两种零样本学习方法，基于嵌入空间线性插值假设，在多数据集实验中，SAP表现出色，组合运动准确率大幅提升，在在线推理实验中优势也得以保持。
中文摘要 AI 辅助
表面肌电（sEMG）可实现假肢控制，使上肢截肢者恢复部分手部功能。当前多数研究聚焦于识别假肢控制的基本运动，而日常活动中组合运动至关重要，但收集所有组合运动训练数据耗时且新组合需重新训练模型。我们提出组合原型插值（CPI）和原型合成适配（SAP）两种新识别方法，仅用基本运动训练后就能在原型网络中对组合、新颖及未见运动进行零样本学习。方法基于嵌入空间的线性插值假设，通过检查信号和嵌入空间中组合运动的几何结构进行研究。在NearLab、NinaPro DB3数据集及新记录的BasCom数据集实验中，我们提出的SAP优于先前零样本学习方法，组合运动准确率提高超20%，在用户研究的在线推理实验中该优势也得以保持。
英文摘要
Surface electromyography (sEMG) enables the control of prostheses, allowing upper-limb amputees to re-gain some hand function. Most current research focuses on recognizing basic movements for prosthesis control. However, in most daily activities, such as opening a door, combined movements are essential. However, collecting training data for all possible combined movements is time-consuming and requires re-training of the model for any new combination. We propose two novel recognition approaches, Compositional Prototype Interpolation (CPI) and Synthetic Adaptation for Prototypes (SAP), that enable zero-shot learning of combined, novel and unseen movements in Prototype Networks after training only with basic movements. Our methods rest on a linear interpolation assumption in the embedding space, which we study by inspecting the geometry of combined motions in signal and embedding space. In experiments on the NearLab and NinaPro DB3 data sets as well as our newly recorded BasCom dataset, our proposed SAP outperforms prior zero-shot learning methods with accuracy improvements on combined movements of more than 20%. This advantage is maintained in online inference experiments in a user study.
发表机构
- Faculty of Technology, Bielefeld University（比勒费尔德大学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
