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Legged locomotion is essential for navigating challenging terrains where conventional robotic systems encounter difficulties. This study investigates the sensitivity of the reconfigurable Klann legged mechanism (KLM) to variations in the input geometric parameters, such as joint position location, link lengths, and angles between linkages, on the continuous coupler curve, which represents the output trace of the leg movement.The continuous coupler curve's sensitivity is explored using global sensitivity analysis based on Sobol's sensitivity method. Furthermore, a novel reconfigurability strategy is presented for the Klann mechanism, aiming to reduce the number of required actuators and the complexity in control. In simulation, the coupler curves obtained from the reconfigurable KLM are classified as hammering, digging, jam avoidance, and step climbing using machine learning approaches. Experimental validation is presented, discussing an approach to identifying geometric parameters and the resultant coupler curve. Illustrations of the the complete assembly of the reconfigured KLM with the obtained gaits using limited experiments are also highlighted. [ABSTRACT FROM AUTHOR]