Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks
Kunihiro Ogata, Yoshio Matsumoto · 2019
To measure the life log of humans and enjoy virtual or augmented reality video games, several wearable devices have been developed that allow users to intuitively input commands. However, monitoring and estimating three-dimensional human motions for extended periods using the wearable devices is difficult. Therefore, this study aims to develop a method that estimates the joint angles of the upper human body using a wearable suit implanted with strain sensors with a nonlinear characteristic. We used a convolutional neural network (CNN) to estimate the joint angles. We established a CNN estimator based on the training data of two adult males and confirmed that this estimator could estimate the joint angles of other adult males. To monitor the caretakers in a care facility, we measure the care-working motion, such as motions that care workers transform the elder persons, estimate each joint angle, and visualize the motions on Unity.