用合成肌电图提升假肢对复杂组合动作的识别能力
Recognition of Unseen Combined Motions via Convex Combination-based EMG Pattern Synthesis for Myoelectric Control
- 通过基础动作的凸组合生成合成肌电信号
- 对未见过的组合动作识别准确率提升约17%
- 适合数据采集困难的假肢控制场景
表面肌电（EMG）信号可实现假肢等辅助设备的直观控制。然而，在基于EMG的动作识别中，获取所有目标动作的完整训练数据仍具挑战性，尤其针对复杂组合动作。本文提出一种方法，通过基础动作模式的凸组合生成合成EMG数据，用于联合动作识别。无需实测所有组合动作，仅需基础动作数据与合成数据即可训练模型，显著减少数据采集量的同时扩展可识别动作范围。在8名受试者上进行上肢动作分类实验，结果表明该方法使未见过的组合动作识别准确率提升约17%。
原文摘要 · Abstract (English)
Electromyogram (EMG) signals recorded from the skin surface enable intuitive control of assistive devices such as prosthetic limbs. However, in EMG-based motion recognition, collecting comprehensive training data for all target motions remains challenging, particularly for complex combined motions. This paper proposes a method to efficiently recognize combined motions using synthetic EMG data generated through convex combinations of basic motion patterns. Instead of measuring all possible combined motions, the proposed method utilizes measured basic motion data along with synthetically combined motion data for training. This approach expands the range of recognizable combined motions while minimizing the required training data collection. We evaluated the effectiveness of the proposed method through an upper limb motion classification experiment with eight subjects. The experimental results demonstrated that the proposed method improved the classification accuracy for unseen combined motions by approximately 17%.
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