Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm. (April 2020)
- Record Type:
- Journal Article
- Title:
- Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm. (April 2020)
- Main Title:
- Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm
- Authors:
- Motao, Wang
Zhijun, Li
Qing, Lei
Meng, Wang
Rui, Zhang - Abstract:
- Abstract: In order to realize the follow-up control of the exoskeleton robot better, the gait phase of the human body should be accurately identified and the human body motion intention should be matched in real time. In this paper, a set of gait data measurement system is used to collect the gait information of the human body during the movement process. Then, the gait recognition of the six models is carried out by the support vector machine through the plantar pressure information. Then the human movement intention is divided into five kinds and the improved DTW algorithm was used to complete the work of matching human motion intentions. Ultimately, the BP neural network model was designed to accurately predict the gait data. The experimental results show that the exoskeleton robot can accurately realize the three functions of recognition, matching and prediction.
- Is Part Of:
- Journal of physics. Volume 1518(2020)
- Journal:
- Journal of physics
- Issue:
- Volume 1518(2020)
- Issue Display:
- Volume 1518, Issue 1 (2020)
- Year:
- 2020
- Volume:
- 1518
- Issue:
- 1
- Issue Sort Value:
- 2020-1518-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-04
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1518/1/012019 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25485.xml