Lower-limb exoskeletons are wearable devices whose main purposes are human rehabilitation and bilateral locomotion assistance. In particular, there is a growing interest for their use to symmetrize the gait of hemiparetic patients. This often consists in using the kinematics of the less affected side as a reference for the most affected one. In this work, we followed this approach to design a symmetrization algorithm using the formalism of motor primitives, i.e. a low-dimensional set of signals that provide the desired assistance through their combination. The amount of variables to be stored in memory is thus intrinsically limited, and this framework is particularly adapted to include other modes of assistance and/or transitions between locomotion tasks. In this paper, we report the preliminary validation of this newly developed algorithm with a hip exoskeleton and a single participant replicating hemiparetic walking. Results show that the algorithm effectively managed to reduce both temporal and spatial gait asymmetry.

Lower-limb exoskeletons are robotic devices that can provide assistance to human locomotion. Since they are expected to be used in ecological environments, their control strategy should handle different kinds of daily-life situations. Taking inspiration from the human neuromuscular system - and particularly from the socalled motor primitives - may help in adapting the type of delivered assistance to different locomotion tasks. In this work, we validated the combination of simplified primitives and a musculoskeletal model for assisting healthy subjects with a hip exoskeleton. This framework showed adaptation to the user's gait for different slope inclinations, although its effects on the subject's speed and their perceived effort showed no significant improvement compared to wearing the device in transparent mode.