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A Locomotion Mode Recognition Algorithm Using Adaptive Dynamic Movement Primitives
- Source :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4318-4328 (2023)
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- Control systems of robotic prostheses should be designed to decode the users’ intent to start, stop, or change locomotion; and to select the suitable control strategy, accordingly. This paper describes a locomotion mode recognition algorithm based on adaptive Dynamic Movement Primitive models used as locomotion templates. The models take foot-ground contact information and thigh roll angle, measured by an inertial measurement unit, for generating continuous model variables to extract features for a set of Support Vector Machines. The proposed algorithm was tested offline on data acquired from 10 intact subjects and 1 subject with transtibial amputation, in ground-level walking and stair ascending/descending activities. Following subject-specific training, results on intact subjects showed that the algorithm can classify initiatory and steady-state steps with up to 100.00% median accuracy medially at 28.45% and 27.40% of the swing phase, respectively. While the transitory steps were classified with up to 87.30% median accuracy medially at 90.54% of the swing phase. Results with data of the transtibial amputee showed that the algorithm classified initiatory, steady-state, and transitory steps with up to 92.59%, 100%, and 93.10% median accuracies medially at 19.48%, 51.47%, and 93.33% of the swing phase, respectively. The results support the feasibility of this approach in robotic prosthesis control.
Details
- Language :
- English
- ISSN :
- 15580210
- Volume :
- 31
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f9231a9de954fa9b9559516cb327849
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/TNSRE.2023.3327751