A novel sEMG-based hand gesture prediction method using a new motion detection algorithm and an LCNN model

This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window to define a new information index in the time domain. Based on this information index, we introduce a new motion detection algorithm that more accurately captures the start and end times of hand gesture motions. Second, we design a new LCNN model, in which LSTM is integrated into the middle of the encoder, allowing for the direct fusion of multi-scale features to prevent the separation of local and temporal features. An ablation study demonstrates that each functional module of the proposed LCNN model positively contributes to the performance of sEMG pattern recognition. The evaluation of the proposed hand gesture prediction method was conducted by comparing it with existing methods using two publicly available datasets. In the experiment involving the dataset Zhang<i>et al</i>(2020<i>Sensors</i>,<b>20</b>3994), the average prediction accuracy for 21 gestures reaches 92.4%. In the experiment with the dataset Krilova<i>et al</i>(2018<i>UCI Machine Learn. Repo.</i>doi: 10.24432/C5ZP5C), the average prediction accuracy for six hand gestures reaches 82.7%. The results of this study indicate that our motion detection algorithm significantly outperforms the threshold method based on a single time-domain information standard deviation (92.4%,<i>p</i>= 0.0136). Furthermore, our LCNN model also surpasses GRU, LSTM, and other models in terms of prediction accuracy and real-time performance. The research results of this paper highlights the superiority in accuracy and real-time performance of our proposed hand gesture prediction method, which holds great potential for practical applications.