This work investigates how arm swing movements measured by Inertial Motion Unit (IMU) sensors can be used to identify and maintain the walking state in a self-balanced lower-limb exoskeleton for medical use. When an exoskeleton is in a dynamical state during gait, short patterns in IMU signals (e.g. a braking movement) can be hard to extract. Therefore, by relying on a threshold-based classifier constructed upon descriptive features of actively maintained arm swing movements, it is possible to build a gait termination detection method in which the transition between the walking and standstill states occurs whenever arm movements cease, and the corresponding patterns in the IMU signals disappear. Analysis of arm IMU signals were used to identify three amplitude and coordination-based features for the classification architecture. An online implementation of this novel detection interface for maintaining the walking state was validated with 11 unimpaired participants using the Atalante exoskeleton, leading to high accuracy with less than 2% of false negatives when the arms were swinging at a high amplitude, and less than 15% when they were swinging at a medium amplitude.