A humanoid control strategy based on deep reinforcement learning for enhanced comfort in lower limb rehabilitation robots.

ABSTRACT: The development of humanoid intelligent controllers represents a breakthrough in enhancing the effectiveness and comfort of rehabilitation training using lower limb rehabilitation robots for diverse gait patterns. In this study, a kinematic model of the lower limb rehabilitation robot is established based on a simplified link structure. For dynamic modeling, the Lagrange formulation is employed to analyze human lower limb motion from an energy-based perspective.Gait data were collected from five healthy subjects, with each performing 20 walking trials, yielding a total of 100 gait cycles for analysis. The acquired gait data are filtered and fitted to plan a reference trajectory of anthropomorphic joint angles suitable for rehabilitation training. Joint torques are computed from plantar for

SUBMITTER: Jin Y 

PROVIDER: S-EPMC12923521 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature