Research on control strategies for rehabilitation robots has gradually shifted from providing therapies with fixed, relatively stiff assistance to compelling alternatives with assistance or challenge strategies to maximize subject participation. These alternative control strategies can promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. In this paper, we propose a control strategy that dynamically switches between assistance and challenge modes based on the user's performance by amplifying or reducing the deviation between the user and the rehabilitation robot. For a seamless cognitive and physical interaction between the robot and the patient, we propose a multisensor fusion system to provide accurate activity and motor capability recognition, fall detection and physical fitness assessment in the rehabilitation training process. Moreover, an adaptive radial basis function (RBF) neural sliding-mode (ARNNSM) controller that dominates a mobile chaperonage lower limb rehabilitation training robot is proposed. The controller employs asynchronous deviation and functional assessment to determine the subject's capabilities and computes a corresponding assistance torque with a challenging factor for the desired locomotive function. Some sufficient conditions are derived based on algebraic graph theory and Lyapunov theory to ensure the asymptotic stability of the systems. Simulation examples illustrate the effectiveness of the proposed controllers. The ARNNSM controller and accompanying algorithm are demonstrated experimentally with healthy subjects in a new type of mobile chaperonage lower limb rehabilitation robot.