2026-10-29T21:48:06Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/12162026-10-28T15:28:34Zopenairedrivercom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Surface EMG-Based Hand Gesture Recognition Using a Hybrid Multistream Deep Learning ArchitectureÇelik, YusufCan, Umitbiometric signal processingdeep learninghand gesture recognitionsurface electromyographySurface electromyography (sEMG) enables non-invasive measurement of muscle activity for applications such as human–machine interaction, rehabilitation, and prosthesis control. However, high noise levels, inter-subject variability, and the complex nature of muscle activation hinder robust gesture classification. This study proposes a multistream hybrid deep-learning architecture for the FORS-EMG dataset to address these challenges. The model integrates Temporal Convolutional Networks (TCN), depthwise separable convolutions, bidirectional Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) layers, and a Transformer encoder to capture complementary temporal and spectral patterns, and an ArcFace-based classifier to enhance class separability. We evaluate the approach under three protocols: subject-wise, random split without augmentation, and random split with augmentation. In the augmented random-split setting, the model attains 96.4% accuracy, surpassing previously reported values. In the subject-wise setting, accuracy is 74%, revealing limited cross-user generalization. The results demonstrate the method’s high performance and highlight the impact of data-partition strategies for real-world sEMG-based gesture recognition.Multidisciplinary Digital Publishing Institute (MDPI)2026Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/1216https://doi.org/10.3390/s260722811424-8220enSensorsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/1216