SparseEMG: A Computational Framework to Design Sparse Electrode Layouts for Sensing Gestures

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 combina-tions (4 selection schemes, 7 classifiers), across six datasets, we identify an approach that minimizes electrode count without compromising accu-racy. 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 gen-erates sparse electrode layouts based on user-selected gesture sets, elec-trode 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.