Variable-Gravity RoboREHAB for Gait and Sit-to-Stand Rehabilitation: A System-Level Integration Study with Simulation and Benchtop Prototype Evidence

Robotic gait rehabilitation can increase training intensity and repeatability, yet many systems still rely on fixed assistance or pre-programmed trajectories and provide limited support for functional transitions such as sit-to-stand and stand-to-sit. This study presents RoboREHAB as a system-level integration effort that combines variable-gravity assistance through joint-level torque compensation, a redesigned leg assembly intended to improve knee kinematics, and motion-capture-informed reinforcement learning as a preliminary personalization layer for trajectory tracking. The manuscript defines a unified architecture with explicit module interfaces and signal flow and distinguishes simulation-based evaluation, benchtop prototype evidence, and future validation steps to maintain traceability and align claims with the current level of validation. Simulation-based evaluation under the present configuration indicated reductions in gait trajectory error (66%), balance recovery time (55%), stride deviation (72%), and joint torque variability (66%) relative to a fixed-gravity, predefined-trajectory baseline under matched simulated scenario definitions, together with trajectory tracking within an approximately 5% error margin for the redesigned assembly relative to motion-capture references. Benchtop prototype demonstrations support the subsystem-level feasibility of sensor-driven variable-gravity control and user-adjustable assistance scaling using an embedded control stack. Overall, the present evidence supports the feasibility of the integrated RoboREHAB architecture and defines a staged validation pathway toward future hardware-in-the-loop testing, instrumented full-scale evaluation, and eventual human-subject studies.