School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130000, China.
Sensors (Basel). 2024 Feb 5;24(3):1032. doi: 10.3390/s24031032.
Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.
人体运动过程中活体肌肉力的估计对于理解人体运动控制机制和关节力学非常重要。本文结合卷积神经网络（CNN）和长短时记忆（LSTM）的优势，提出了一种基于 CNN-LSTM 的新型肌肉力估计方法。还开发了一种可穿戴传感器系统，用于在矢状面内收集行走过程中髋关节、膝关节和踝关节的角度和角速度，所收集的运动学数据作为神经网络模型的输入。在本文中，使用 OpenSim 基于静态优化（SO）方法计算的肌肉力作为标准值来训练神经网络模型。选择左腿的四条下肢肌肉，包括臀大肌（GM）、股直肌（RF）、腓肠肌（GAST）和比目鱼肌（SOL）作为研究对象。实验结果表明，与标准 CNN 和标准 LSTM 相比，CNN-LSTM 在慢（1.2m/s）、中（1.5m/s）和快（1.8m/s）行走速度下的肌肉力估计表现更好。在慢、中、快三种行走速度下，四条肌肉力的真实值和估计值之间的平均相关系数分别为 0.9801、0.9829 和 0.9809。在不同行走速度下，平均相关系数的波动较小，这表明该模型具有良好的鲁棒性。外部测试实验表明，CNN-LSTM 也具有良好的泛化能力。当估计对象不在训练样本中时，模型表现良好。本文提出了一种方便的肌肉力估计方法，可为人体运动和肌肉损伤的定量分析提供理论帮助。该方法基于神经网络模型建立了行走过程中关节运动信号与肌肉力之间的关系；与 OpenSim 中 SO 方法计算肌肉力相比，在临床分析或工程应用中更加方便和高效。
