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Interactive control schemes are rapidly gaining popularity in the control of robotic gait trainers. Interactive control allows for the modification of the support level based on the patient's performance. However, only few algorithms exist that adapt the support to the patient's needs. The aim of this study was to assess the feasibility of an adaptive and selective method to support a specific subtask of walking. In this study we focused on providing assistance during foot clearance and analyzed the effects in four chronic stroke survivors whose gait is characterized as stiff knee gait. We recently introduced a method to selectively support the foot clearance by defining a virtual spring between the desired and the actual ankle height. Here, this method was extended with an algorithm that automatically adapts the stiffness of the virtual spring, and consequently, adapts the amount of support to the experienced movement error in the previous steps. The results showed that the stiffness profile converged to a subject specific pattern that varied over the gait cycle and was according to the subject's requirements. The proposed algorithm was used in a training study that specifically aimed at increasing the foot clearance. Preliminary results demonstrated that the training resulted in improved foot clearance, which was accompanied by an increased walking speed. This proposed algorithm reduces the need for the therapist/operator to set the amount of support on a trial and error basis and decreases the chances of reliance on the robotic support.