TY - Jour A2 - Albuquerque，Victor Au - He，Jie Au - Guo，Zhiaoo Au - Shao，Ziwei Au - Zhao，Juhao Au - Dan，Guo Py - 2020 DA - 2020/07/23 Ti - 基于LSTM的预测通过Kinect Visual Signal SP-8024789 VL-2020 AB的整合分析较低的肢体意图感知 - 最近，在各种步态康复研究中应用了计算机视觉和深度学习技术。考虑到长期内存（LSTM）网络已经证明了在学习序列特征表示中的出色性能，我们提出了一种基于LSTM的下肢关节轨迹预测方法，用于对康复机器人系统进行积极的康复。Our approach based on synergy theory exploits that the follow-up lower limb joint trajectory, i.e. limb intention, could be generated by joint angles of the previous swing process of upper limb which were acquired from Kinect platform, an advanced computer vision platform for motion tracking. A customize Kinect-Treadmill data acquisition platform was built for this study. With this platform, data acquisition on ten healthy subjects is processed in four different walking speeds to acquire the joint angles calculated by Kinect visual signals of upper and lower limb swing. Then, the angles of hip and knee in one side which were presented as lower limb intentions are predicted by the fore angles of the elbow and shoulder on the opposite side via a trained LSTM model. The results indicate that the trained LSTM model has a better estimation of predicting the lower limb intentions, and the feasibility of Kinect visual signals has been validated as well. SN - 2040-2295 UR - https://doi.org/10.1155/2020/8024789 DO - 10.1155/2020/8024789 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -