Echo-based dynamic trajectory generation for customised unilateral exoskeleton applications

For unilateral pathologies, effective rehabilitation relies on the use of a customised trajectory in order for the user to relearn a natural and symmetrical gait. In recent years, lower-limb exoskeletons have seen a growing interest due to their capacity to provide support and facilitate repetitive exercises while correcting the user’s motion. However, in the context of robotic-assisted locomotion, the investigated trajectory models tend to rely on generating standardised walking patterns that lack step-specific customisation, and therefore do not account for the dynamic variations of natural gait.This paper investigates the viability of an echo-based approach for trajectory generation, which centres around the dynamic relabelling of a time-invariant reference trajectory, based on the motion of the contralateral leg. The presented cascaded network combines (1) a classifier that determines the gait phase performed by the sound leg and updates the reference trajectory accordingly, with (2) a regressor that uses electromyography inputs from the investigated leg to predict the gait cycle percentage performed, and provide the associated knee angle based on the dynamic reference.This trajectory generation framework was evaluated on 6 able-bodied subjects, using both steady-state and transient speeds. Despite some discrepancies in the range of motion, the produced knee angle trajectory strongly resembles the experimentally captured ones for both conditions, with an average mapping Root Mean Squared Error across subjects of 4.62°±0.39° for steady-state and 5.88°±1.83°for transient speeds. This proof-of-concept implementation demonstrates the potential of an echo-based approach for personalised dynamic trajectory generation in unilateral exoskeleton applications.