Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks
Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks
复制标题
基于卷积神经网络的应变传感器上的衬衫式可穿戴设备的人体运动估计
DOI：
10.1109/embc.2019.8856722
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Y. Matsumoto
中科院分区：
文献类型：
--
作者：
Kunihiro Ogata;Y. Matsumoto
为了测量人类的生活日志并享受虚拟或增强现实视频游戏，已经开发了几种允许用户直观地输入命令的可穿戴设备。然而，使用可穿戴设备长时间地监测和估计三维人体运动是困难的。因此，本研究的目的是开发一种方法，估计关节角度的上半身使用植入应变传感器具有非线性特性的可穿戴式西装。我们使用卷积神经网络（CNN）来估计关节角度。我们基于两个成年男性的训练数据建立了CNN估计器，并证实该估计器可以估计其他成年男性的关节角度。为了监控护理机构中的护理人员，我们测量护理工作运动，例如护理人员转变老年人的运动，估计每个关节角度，并在Unity上可视化运动。
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.