Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3628-3631. doi: 10.1109/EMBC48229.2022.9871089.
In the expanding field of robotic prosthetics, surface electromyography (sEMG) signals can be decoded to seamlessly control a robotic prosthesis to perform the desired gesture. It is essential to create a pipeline, which can acquire, process, and accurately classify sEMG signals in order to replicate the desired hand gesture in near real-time and in a reliable manner. In this study, an optimised pipeline is proposed. This pipeline encompasses the main stages of sEMG signal processing and hand gesture classification and implements a sliding window approach, which is the main focus of the optimisation. In this study, a range of different parameters and modelling approaches are evaluated. The main contributions of this work are a robust and extensive analysis of sliding window parameter selection and an optimised pipeline that could be implemented in practice with minimal overheads. The optimum pipeline is efficient and achieves accurate prediction of hand gestures with an uninterrupted processing pipeline.
在不断发展的机器人假肢领域，表面肌电 (sEMG) 信号可以被解码，以无缝控制机器人假肢执行所需的手势。创建一个能够获取、处理和准确分类 sEMG 信号的管道至关重要，以便以可靠的方式近乎实时地复制所需的手势。在本研究中，提出了一种优化的管道。该管道包括 sEMG 信号处理和手势分类的主要阶段，并实现了滑动窗口方法，这是优化的主要重点。在本研究中，评估了一系列不同的参数和建模方法。这项工作的主要贡献是对滑动窗口参数选择进行了稳健而广泛的分析，并提出了一种优化的管道，该管道可以在实际中以最小的开销进行实施。最优的管道是高效的，并且具有不间断的处理管道，能够实现对手势的准确预测。
