Continuous Phase Estimation in a Variety of Locomotion Modes Using Adaptive Dynamic Movement Primitives.
IEEE Int Conf Rehabil Robot
; 2023: 1-6, 2023 09.
Article
en En
| MEDLINE
| ID: mdl-37941254
ABSTRACT
Accurate gait phase estimation algorithms can be used to synchronize the action of wearable robots to the volitional user movements in real time. Current-day gait phase estimation methods are designed mostly for rhythmic tasks and evaluated in highly controlled walking environments (namely, steady-state walking). Here, we implemented adaptive Dynamic Movement Primitives (aDMP) for continuous real-time phase estimation in the most common locomotion activities of daily living, which are level-ground walking, stair negotiation, and ramp negotiation. The proposed method uses the thigh roll angle and foot-contact information and was tested in real time with five subjects. The estimated phase resulted in an average root-mean-square error of 3.98% ± 1.33% and a final estimation error of 0.60% ± 0.55% with respect to the linear phase. The results of this study constitute a viable groundwork for future phase-based control strategies for lower-limb wearable robots, such as robotic prostheses or exoskeletons.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Actividades Cotidianas
/
Locomoción
Límite:
Humans
Idioma:
En
Revista:
IEEE Int Conf Rehabil Robot
Año:
2023
Tipo del documento:
Article