Back to Search
Start Over
Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots.
- Source :
- IEEE/ASME Transactions on Mechatronics; Dec2017, Vol. 22 Issue 6, p2480-2491, 12p
- Publication Year :
- 2017
-
Abstract
- Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy-logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10 000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10834435
- Volume :
- 22
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE/ASME Transactions on Mechatronics
- Publication Type :
- Academic Journal
- Accession number :
- 126820112
- Full Text :
- https://doi.org/10.1109/TMECH.2017.2755048