Continuous dynamic gesture recognition using surface EMG signals based on blockchain-enabled internet of medical things

The integration of blockchain technology in the Internet of Medical Things (IoMT) enhances user security, convenience and interoperability. It introduces a novel approach to gesture recognition. Dynamic surface electromyography (EMG) is essential to address the shortcomings of visual analysis and gesture similarity in discrete pattern recognition. This paper uses an improved comb filter and a Gaussian mixture model to reduce the noise of surface EMG signals. Continuous dynamic gesture recognition models have been improved based on temporal network and generalized regression neural network. These models are applied to blockchain-enabled IoMT and ensure the traceability of data sharing. Experimental results demonstrate that the proposed method effectively reduces the false recognition rate attributed to signal complexity, thus achieving accurate continuous dynamic gesture recognition. This paper's approach lays a crucial foundation for implementing it in the blockchain-enabled IoMT.

Automated summary

This paper explores the integration of blockchain technology in the Internet of Medical Things (IoMT) and its impact on user security, convenience, and interoperability. It presents a novel approach to continuous dynamic gesture recognition by improving filters and models for surface electromyography (EMG) signals. The experimental results demonstrate the effectiveness of the proposed method in reducing false recognition rates and achieving accurate gesture recognition.