Leveraging Agonistic-Antagonistic Coactivation in Single-Grid HDsEMG for Hand Gesture Recognition

Surface Electromyography (sEMG) is critical for intention prediction in human-computer interfaces, such as for prosthetics control. Although deep learning models for Hand Gesture Recognition (HGR) yield excellent results, they impose high computational and hardware demands. This paper addresses this bottleneck by exploiting redundancies in agonist-antagonist muscle activity, hypothesizing that coactivations present in the sEMG signals from the extensor or flexor groups alone are sufficient for accurate HGR. We evaluate this by comparing convolutional neural networks (CNNs) trained on one muscle grid against CNN architectures trained jointly on both grids. Experiments were conducted using 16 gestures from a dataset of high-density sEMG signals from 20 subjects. The results demonstrate that the extensor grid alone achieves performance (89.5% balanced accuracy, 0.99 AUROC) comparable to the dual-grid system (94.6% balanced accuracy, 1.00 AUROC). Notably, even when applying slow joint fusion to capture spatial features across grids, model performance did not improve. GradCAM visualizations and anatomical analysis further indicate that the extensor region provides superior signal quality compared to the flexors. Our findings suggest that for a base set of DoF gestures, HGR hardware requirements and computational complexity can be halved without a prohibitive loss in accuracy.

From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics

In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces function by decoding signals captured non-invasively from the skin's surface. Portable high-density surface electromyography (HD-sEMG) modules combined with deep learning decoding have attracted interest by achieving excellent gesture prediction and myoelectric control of prosthetic systems and neurorobots. However, factors like pixel-shape electrode size and unstable skin contact make HD-sEMG susceptible to pixel electrode drops. The sparse electrode-skin disconnections rooted in issues such as low adhesion, sweating, hair blockage, and skin stretch challenge the reliability and scalability of these modules as the perception unit for neurorobotic systems. This paper proposes a novel deep-learning model providing resiliency for HD-sEMG modules, which can be used in the wearable interfaces of neurorobots. The proposed 3D Dilated Efficient CapsNet model trains on an augmented input space to computationally `force' the network to learn channel dropout variations and thus learn robustness to channel dropout. The proposed framework maintained high performance under a sensor dropout reliability study conducted. Results show conventional models' performance significantly degrades with dropout and is recovered using the proposed architecture and the training paradigm.