Multi-Scale Attention Fusion Gesture-Recognition Algorithm Based on Strain Sensors

Surface electromyography (sEMG) signals are commonly employed for dynamic-gesture recognition. However, their robustness is often compromised by individual variability and sensor placement inconsistencies, limiting their reliability in complex and unconstrained scenarios. In contrast, strain-gauge signals offer enhanced environmental adaptability by stably capturing joint deformation processes. To address the challenges posed by the multi-channel, temporal, and amplitude-varying nature of strain signals, this paper proposes a lightweight hybrid attention network, termed MACLiteNet. The network integrates a local temporal modeling branch, a multi-scale fusion module, and a channel reconstruction mechanism to jointly capture local dynamic transitions and inter-channel structural correlations. Experimental evaluations conducted on both a self-collected strain-gauge dataset and the public sEMG benchmark NinaPro DB1 demonstrate that MACLiteNet achieves recognition accuracies of 99.71% and 98.45%, respectively, with only 0.22M parameters and a computational cost as low as 0.10 GFLOPs. Extensive experimental results demonstrate that the proposed method achieves superior performance in terms of accuracy, efficiency, and cross-modal generalization, offering a promising solution for building efficient and reliable strain-driven interactive systems.