Enhanced EMG-Based Hand Gesture Classification in Real-World Scenarios: Mitigating Dynamic Factors With Tempo-Spatial Wavelet Transform and Deep Learning

Abstract

Dynamic factors, like limb position changes and electrode shifting, significantly impact the performance of EMG-based hand gesture classification as the transition is made from a laboratory-controlled environment to real-life scenarios. Traditionally, researchers have employed conventional wavelet transform methods to improve classification performance. This study compares a tempo-spatial technique that utilizes the wavelet multiresolution method and compares it with the conventional wavelet transform using eight machine learning algorithms. Two public datasets are utilized. DB1 comprising ideal conditions with a range of limb positions, while DB2 incorporates dynamic factors like electrode shifting and muscle fatigue. The training/testing involves two cases: one using single-position data and other with multiple positions. Results demonstrate that the Deep Neural Network (DNN) classifier outperforms others in classification accuracy. Proposed technique achieves mean accuracies of 84.07% (DB1) and 68.15% (DB2), while conventional wavelet transform methods achieve 79.39% (DB1) and 53.48% (DB2) for single-position DNN training. For multiple positions, particularly two limb positions, the proposed technique achieves mean accuracies of 94.43% (DB1) and 73.79% (DB2), compared to conventional wavelet transform, which achieves 84.38% (DB1) and 51.98% (DB2) with DNN. Paired t-tests (p-value<0.001) show significant improvement over conventional wavelet transformation, indicating the proposed technique’s potential to enhance gesture classification in real-world scenarios.