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Objective: Multimodal hand gesture recognition (HGR) systems can achievehigher recognition accuracy compared to unimodal HGR systems. However,acquiring multimodal gesture recognition data typically requires users to wearadditional sensors, thereby increasing hardware costs. Methods: This paperproposes a novel generative approach to improve Surface Electromyography(sEMG)-based HGR accuracy via virtual Inertial Measurement Unit (IMU) signals.Specifically, we trained a deep generative model based on the intrinsiccorrelation between forearm sEMG signals and forearm IMU signals to generatevirtual forearm IMU signals from the input forearm sEMG signals at first.Subsequently, the sEMG signals and virtual IMU signals were fed into amultimodal Convolutional Neural Network (CNN) model for gesture recognition.Results: We conducted evaluations on six databases, including five publiclyavailable databases and our collected database comprising 28 subjectsperforming 38 gestures, containing both sEMG and IMU data. The results showthat our proposed approach significantly outperforms the sEMG-based unimodalHGR approach (with increases of 2.15%-13.10%). Moreover, it achieves accuracylevels closely matching those of multimodal HGR when using virtual Acceleration(ACC) signals. Conclusion: It demonstrates that incorporating virtual IMUsignals, generated by deep generative models, can significantly improve theaccuracy of sEMG-based HGR. Significance: The proposed approach represents asuccessful attempt to bridge the gap between unimodal HGR and multimodal HGRwithout additional sensor hardware, which can help to promote furtherdevelopment of natural and cost-effective myoelectric interfaces in thebiomedical engineering field.

From Unimodal to Multimodal: improving sEMG-Based Pattern Recognition via deep generative models