这篇论文提出了两种新方法，只用基本动作训练就能识别组合动作，在多个数据集上准确率提升超20%，挺实用。
表面肌电（sEMG）用于控制假肢，但现有研究多聚焦基本动作，而日常活动需组合动作。论文提出CPI和SAP两种原型网络方法，实现仅用基本动作训练后零样本识别未见组合动作。在NearLab、NinaPro DB3及新采集的BasCom数据集上，SAP准确率较现有零样本方法提升超过20%。用户在线实验也验证了优势。
Prototype Adaptation for Zero-Shot sEMG Movement Classification
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.