School of Engineering, Monash University Malaysia, Subang Jaya 47500, Malaysia.
Department of Electrical & Computer Engineering, Curtin University Malaysia, Miri 98009, Malaysia.
Sensors (Basel). 2023 Jan 3;23(1):556. doi: 10.3390/s23010556.
The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.
惯性测量单元（IMU）在步态分析中越来越普及。然而，它只能测量与之相连的身体部位的运动学。肌肉行为是步态分析的重要组成部分，它提供了对步态质量的更全面的了解。肌肉行为可以使用肌肉骨骼建模来估计，也可以使用肌电图（EMG）来测量。然而，这两种方法都可能具有挑战性且资源密集。IMU 和神经网络（NN）的结合有可能克服这一限制。因此，本研究提出使用 NN 和 IMU 数据来估计九个下肢肌肉活动。开发并研究了两种 NN，即前馈神经网络（FNN）和长短期记忆神经网络（LSTM）。结果表明，虽然两种网络都能很好地预测肌肉活动，但 LSTM 优于传统的 FNN。本研究证实了使用 IMU 数据和 NN 估计肌肉活动的可行性。它还表明，这种方法有可能使步态分析能够在实验室环境之外、使用有限数量的设备进行。
