An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal
Joint Authors
Guo, Zhexiao
Shao, Ziwei
Zhao, Junhao
Dan, Guo
He, Jie
Source
Journal of Healthcare Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Recently, computer vision and deep learning technology has been applied in various gait rehabilitation researches.
Considering the long short-term memory (LSTM) network has been proved an excellent performance in learn sequence feature representations, we proposed a lower limb joint trajectory prediction method based on LSTM for conducting active rehabilitation on a rehabilitation robotic system.
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.
American Psychological Association (APA)
He, Jie& Guo, Zhexiao& Shao, Ziwei& Zhao, Junhao& Dan, Guo. 2020. An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1186401
Modern Language Association (MLA)
He, Jie…[et al.]. An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal. Journal of Healthcare Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1186401
American Medical Association (AMA)
He, Jie& Guo, Zhexiao& Shao, Ziwei& Zhao, Junhao& Dan, Guo. An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1186401
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186401