使用超声图像估计腕关节角度的连续数据
Estimating continuous data of wrist joint angles using ultrasound images
AI总结：
本研究提出使用超声图像结合多元线性回归模型估计连续腕关节角度，实验表明该方法相比肌电图等信号具有更高的估计精度且计算成本低。
摘要：
超声成像最近被引入作为关节运动估计的传感接口。将超声图像用作估计方法有望提高辅助设备和人机界面的控制性能。本研究旨在使用超声图像估计连续的腕关节角度。具体而言，在一项实验中，获取了伸展-屈曲运动期间的关节角度信息，并采集了相关肌肉的超声图像。利用从超声图像中提取的特征，使用多元线性回归模型来估计关节角度。通过光流法从超声图像中获取的特征点坐标被用作多元线性回归模型的自变量。该模型针对每位参与者的每次试验分别进行了训练和测试，以验证估计精度。结果表明，所有试验的估计精度均值和标准差分别为均方根误差（RMSE）= 1.82 ± 0.54 度和决定系数（R2）= 0.985 ± 0.009。与以往使用表面肌电图等其他信号的研究相比，我们的方法实现了对关节角度的高精度估计，同时多元线性回归模型结构简单，且计算和模型训练成本均较低。
英文摘要：
Ultrasound imaging has recently been introduced as a sensing interface for joint motion estimation. The use of ultrasound images as an estimation method is expected to improve the control performance of assistive devices and human--machine interfaces. This study aimed to estimate continuous wrist joint angles using ultrasound images. Specifically, in an experiment, joint angle information was obtained during extension--flexion movements, and ultrasound images of the associated muscles were acquired. Using the features obtained from ultrasound images, a multivariate linear regression model was used to estimate the joint angles. The coordinates of the feature points obtained using optical flow from the ultrasound images were used as explanatory variables of the multivariate linear regression model. The model was trained and tested for each trial by each participant to verify the estimation accuracy. The results show that the mean and standard deviation of the estimation accuracy for all trials were root mean square error (RMSE)=1.82 $\pm$ 0.54 deg and coefficient of determination (R2)=0.985 $\pm$ 0.009. Our method achieves a highly accurate estimation of joint angles compared with previous studies using other signals, such as surface electromyography, while the multivariate linear regression model is simple and both computational and model training costs are low.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
