Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.
Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.
J Biomech. 2024 Apr;167:112093. doi: 10.1016/j.jbiomech.2024.112093. Epub 2024 Apr 11.
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.
一般来说，肌肉活动可以通过肌电图（EMG）直接测量，也可以通过肌肉骨骼模型计算。然而，这两种方法都不适合非技术用户和非结构化环境。人们希望建立更便携和易于使用的肌肉活动估计方法。深度学习（DL）模型结合惯性测量单元（IMU）已显示出估计肌肉活动的巨大潜力。然而，在临床情况下，经常会出现可用数据非常少的情况，导致 DL 模型的性能有限，而很少使用有效的扩充技术来有效地扩展 DL 模型训练的小样本量。本研究的主要目的是开发一种新的 DL 模型，使用 IMU 以高精度估计步态中的 EMG 包络。次要目的是开发一种新的基于模型的数据扩充方法，以提高小规模数据集的估计模型的性能。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，用于数据扩充。此外，还开发了一种无监督回归 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。皮尔逊相关系数和归一化均方根误差从提出的方法达到了 0.72 和 0.13，分别。这表明 MuscleGAN 确实提高了下肢 EMG 包络的估计精度，从 70%提高到 72%。因此，即使仅使用两个 IMU 和一个非常小的数据集，所提出的模型仍然能够准确地估计下肢 EMG 包络，显示出在临床和日常生活场景中应用的巨大潜力。
