Laboratory of Movement Analysis (LAM-Motion Lab), University of Liège, Liège, Belgium.
Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan.
Sci Rep. 2024 Jan 23;14(1):2020. doi: 10.1038/s41598-024-52405-9.
Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human-computer interfaces.
深度神经网络（DNN）在实现稳健肌电控制（MEC）系统方面的性能优于传统方法。然而，优化 MEC 所带来的延迟仍然是实时应用的一个关注点。因此，需要一种基于优化超参数的优化 DNN 架构。本研究通过提出一种有效的数据分段技术和一组通用的超参数，来研究基于卷积神经网络（CNN）的 MEC 的最佳配置。首先，研究了两种分段策略（不相交和重叠）和各种分段和重叠大小，以优化分段参数。其次，为了解决基于 DNN 的 MEC 系统的超参数优化问题，将该问题抽象为一个优化问题，并使用贝叶斯优化来解决该问题。从 20 位健康人中，选择了日常生活中提取的 10 个表面肌电（sEMG）抓取运动作为目标手势集。在理想的分段大小为 200ms，重叠大小为 80%的情况下，结果表明重叠分段技术优于不相交分段技术（p 值<0.05）。与手动（12.76±4.66）、网格（0.10±0.03）和随机（0.12±0.05）搜索超参数优化策略相比，所提出的优化技术在所有受试者中产生了 0.08±0.03 的平均分类错误率（CER）。此外，还提出了一种具有最优超参数集的广义 CNN 架构。当在所有个体上分别进行测试时，单个广义 CNN 架构的总体 CER 为 0.09±0.03。本研究的意义在于通过展示重叠分段技术的优越性、通过贝叶斯优化优化 CNN 超参数以及为改善假肢控制和人机接口提供实际见解，为肌电信号处理领域做出了贡献。
