Abstract
This paper presents a realtime locomotion mode recognition method for an active pelvis orthosis. Five locomotion modes, including sitting, standing still, level-ground walking, ascending stairs, and descending stairs, are taken into consideration. The recognition is performed with locomotion information measured by the onboard hip angle sensors and the pressure insoles. These five modes are firstly divided into static modes and dynamic modes, and the two kinds are classified by monitoring the variation of the relative hip angles of the two legs within a pre-defined period. Static states are further classified into sitting and standing still based on the absolute hip angle. As for dynamic modes, a fuzzy-logic based method is proposed for the recognition. Two event-based locomotion features, including the hip joint angle at the first foot-strike and the center of foot pressure at the first foot-strike are used to calculate the membership of different modes based on the membership function, and the mode with the maximal membership is selected as the target mode. Experimental results with three subjects achieve an average recognition accuracy of 99.87% and average recognition delay of 18.12% of one gait cycle.
Original language | English |
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Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6196-6201 |
Number of pages | 6 |
Volume | 2015-December |
ISBN (Print) | 9781479999941 |
DOIs | |
Publication status | Published - Dec 11 2015 |
Event | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany Duration: Sept 28 2015 → Oct 2 2015 |
Other
Other | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 |
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Country/Territory | Germany |
City | Hamburg |
Period | 9/28/15 → 10/2/15 |
Keywords
- Actuators
- Aging
- Foot
- Hip
- Legged locomotion
- Pelvis
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications