AGMMLN: An Attention-Guided Multiscale Multitask Learning Network for Simultaneous Gesture and Force Level Recognition
Zhangyi Chen, Yilin Yu, Long Wang, Shanjun Zhou, Kai Wang, Hongwei Li, Xiaoling Li · IEEE Sensors Journal · 2024
Gesture recognition technology based on surface electromyography (sEMG) has attracted widespread attention and research. However, most studies have been limited to gesture recognition at a single force level, ignoring the importance of force level recognition during interaction, leading to limitations in its application. Therefore, an attention-guided multiscale multitask learning network (AGMMLN) is proposed in this article. With the Log-Mel time-frequency spectrum of multichannel nonstationary sEMG signals as input, AGMMLN can recognize gestures and force levels simultaneously and accurately. Given the limitation of fixed-scale analysis of classical convolutional neural networks (CNNs), a multiscale convolution module (MSCM) is proposed to simultaneously extract both fine-grained local features and extensive global features in the temporal spectrum. To filter out redundant information in multiscale features and enhance key features, a group convolution module (GCM) is proposed to facilitate nonlinear interactions between feature channels and enhance feature representation. In addition, to automatically select features beneficial to a specific task from the rich set of shared features, a feature attention module (FAM) is proposed to enhance useful features and suppress useless ones. Experiments are conducted on a self-collected able-bodied dataset and a publicly available amputee dataset to validate the effectiveness of the proposed network. The experimental results show that the proposed method achieves 95.19% and 89.91%, and 90.78% and 75.96% accuracy in the simultaneous recognition of gesture and force levels in able-bodied and amputee subjects, respectively, which is significantly superior to the other compared methods.