In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios.

中文翻译：

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使用具有卷积注意神经网络和小规模数据集的惯性测量单元估计步态期间的电肌肉活动

一般来说，肌肉活动可以使用肌电图 (EMG) 直接测量或使用肌肉骨骼模型计算。然而，这两种方法都不适合非技术用户和非结构化环境。期望建立更便携且易于使用的肌肉活动估计方法。深度学习 (DL) 模型与惯性测量单元 (IMU) 相结合，显示出估计肌肉活动的巨大潜力。然而，在临床场景中，经常会出现可用数据量非常少而导致深度学习模型性能有限的情况，而有效扩展小样本量进行深度学习模型训练的增强技术却很少使用。本研究的主要目的是开发一种新颖的深度学习模型，以使用 IMU 高精度估计步态期间的肌电图包络。第二个目标是开发一种新颖的基于模型的数据增强方法，以提高小规模数据集估计模型的性能。因此，在本研究中，提出了一种基于时间卷积网络的生成对抗网络，即MuscleGAN，用于数据增强。此外，还开发了一个独立于受试者的回归 DL 模型来估计 EMG 包络。结果表明，所提出的两阶段方法比常用的现有方法具有更好的泛化和估计性能。该方法得出的皮尔逊相关系数和归一化均方根误差分别达到0.72和0.13。表明MuscleGAN确实将下肢肌电图包络的估计精度从70%提高到了72%。因此，即使仅使用两个 IMU 和非常小的数据集，所提出的模型仍然能够准确估计下肢肌电图包络，展示了其在临床和日常生活场景中的巨大应用潜力。