Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7\% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

翻译：肌电图（EMG）在关键的生物医学领域（如假肢、辅助与交互技术）中得到了广泛应用。本文提出了一种名为ConSGruNet的新型混合神经网络，用于实现精确且高效的手势识别。该模型由带有智能跳跃连接的卷积神经网络与门控循环单元（GRU）结合构成。该模型在完整的Ninapro DB1数据集上进行训练，在仅25毫秒内对53个类别的分类准确率达到99.7%。除了速度快之外，该模型还具有轻量化的特点，大小仅为3,946 KB。此外，该模型还针对可靠性参数（即Cohen's kappa系数、Matthew's相关系数和置信区间）进行了评估。这些参数接近理想的结果验证了模型在未见数据上的性能。