Continuous Phase Estimation in a Variety of Locomotion Modes Using Adaptive Dynamic Movement Primitives

• Published in: IEEE International Conference on Rehabilitation Robotics Vol. 2023; pp. 1 - 6
• Main Authors: Eken, Huseyin, Pergolini, Andrea, Mazzarini, Alessandro, Livolsi, Chiara, Fagioli, Ilaria, Penna, Michele Francesco, Gruppioni, Emanuele, Trigili, Emilio, Crea, Simona, Vitiello, Nicola
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.01.2023
• ISSN: 1945-7901; 1945-7901
• DOI: 10.1109/ICORR58425.2023.10304682

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.