4.6 Article

Estimation of joint torques using an artificial neural network model based on kinematic and anthropometric data

期刊

NEURAL COMPUTING & APPLICATIONS
卷 35, 期 17, 页码 12513-12529

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s00521-023-08379-2

关键词

Human limbs; Back-propagation; Inverse dynamics; Sit to stand

向作者/读者索取更多资源

Joint torques are important in studying human movements. Indirect methods, such as mathematical techniques and artificial neural networks, are commonly used to estimate joint torques. In this study, a four-layer neural network was trained to estimate joint torques using input data from angular displacements, segment heights, and segment mass.
Joint torques are an important parameter in the mechanical study of human movements. People's mass properties and movement patterns have different effects on joint torques. As for human segments, measuring joint torques directly limits movement. Therefore, it is a more common practice to determine joint torques indirectly. Mathematical methods have been successfully used to indirectly determine joint torques. However, mathematical techniques can be challenging. Another way to identify joint torques is to use artificial intelligence techniques. In recent years, it has been seen that joint torque estimation algorithms based on artificial neural networks (ANN) give successful results. Electromyography (EMG) is widely used as input data in estimating joint torque with ANN. Obtaining EMG data is a difficult process and requires sensitive sensors. Data variability is an important factor for the joint torque estimation based on the ANN method. It has been seen that some studies have a limited number of participants with similar physical characteristics. In this study, the analysis of sit-to-stand movement was performed on 20 participants with different physical properties. Then, joint torques were calculated with the simulation model. After that, a four-layer neural network was trained using the angular displacements of the joints, segment heights, and segment mass as input data. Here, different ANN model variations were tested in terms of performance, and the best one was selected. It has been seen that the proposed ANN model shows high accuracy in estimating joint torques using a non-complex method.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据