Title: SparseEMG: Computational Design of Sparse EMG Layouts for Sensing Gestures Show Chinese title

Title: 稀疏EMG：用于感知手势的稀疏EMG布局的计算设计

Abstract: Gesture recognition with electromyography (EMG) is a complex problem influenced by gesture sets, electrode count and placement, and machine learning parameters (e.g., features, classifiers). Most existing toolkits focus on streamlining model development but overlook the impact of electrode selection on classification accuracy. In this work, we present the first data-driven analysis of how electrode selection and classifier choice affect both accuracy and sparsity. Through a systematic evaluation of 28 combinations (4 selection schemes, 7 classifiers), across six datasets, we identify an approach that minimizes electrode count without compromising accuracy. The results show that Permutation Importance (selection scheme) with Random Forest (classifier) reduces the number of electrodes by 53.5\%. Based on these findings, we introduce SparseEMG, a design tool that generates sparse electrode layouts based on user-selected gesture sets, electrode constraints, and ML parameters while also predicting classification performance. SparseEMG supports 50+ unique gestures and is validated in three real-world applications using different hardware setups. Results from our multi-dataset evaluation show that the layouts generated from the SparseEMG design tool are transferable across users with only minimal variation in gesture recognition performance. Show Chinese abstract

Abstract: 使用肌电图（EMG）进行手势识别是一个受手势集、电极数量和位置以及机器学习参数（例如，特征、分类器）影响的复杂问题。 现有的大多数工具包专注于简化模型开发，但忽略了电极选择对分类准确率的影响。 在本工作中，我们提出了第一个数据驱动的分析，研究电极选择和分类器选择如何影响准确率和稀疏性。 通过在六个数据集上对28种组合（4种选择方案，7种分类器）进行系统评估，我们确定了一种在不牺牲准确率的情况下最小化电极数量的方法。 结果表明， 排列重要性（选择方案）与随机森林（分类器）将电极数量减少了53.5%。 基于这些发现，我们引入了SparseEMG，这是一种设计工具，可以根据用户选择的手势集、电极约束和ML参数生成稀疏电极布局，同时预测分类性能。 SparseEMG支持50多个独特的手势，并在三种使用不同硬件设置的真实应用场景中进行了验证。 我们多数据集评估的结果显示，从SparseEMG设计工具生成的布局在不同用户之间具有可移植性，仅在手势识别性能上有很小的变化。