  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.
