SparseEMG: Computational Design of Sparse EMG Layouts for Sensing Gestures

2025-08-07

AI Summary

This study addresses the trade-off between electrode count and classification accuracy in electromyography (EMG)-based gesture recognition. We propose a data-driven sparse electrode placement optimization method that jointly models electrode selection and classifier (random forest) performance. Electrode contributions are quantified via permutation importance, and a cross-dataset performance prediction model is constructed to enable personalized layout generation with cross-subject and cross-hardware generalizability. Evaluated on a 50-class gesture recognition task, our method reduces electrode count by 53.5% on average without significant accuracy degradation. The optimized layouts demonstrate robust generalization across diverse experimental scenarios and multiple commercial EMG hardware platforms. Our key contribution is the first end-to-end electrode-classifier co-optimization framework, coupled with a deployable sparse electrode design tool for practical myoelectric interfaces.

Technology Category

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Application Category

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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.

Problem

• Optimizing electrode selection for accurate gesture recognition
• Minimizing electrode count without sacrificing classification accuracy
• Designing transferable sparse EMG layouts for diverse gesture sets

Innovation

• Uses Permutation Importance for electrode selection
• Combines Random Forest classifier for accuracy
• Generates sparse layouts with SparseEMG tool