Effect of an Imperfect Algorithm on Human Gait Strategies With an Active Ankle Exoskeleton

Lower-limb active exoskeletons may experience errors in operational settings due to imperfect algorithms, which may impact users' trust in the system and the human-exoskeleton fluency (the coordination of actions between the human and exoskeleton). In this study, we introduced pseudorandom catch trials (errors) in 1.68% of all strides, where an expected exoskeleton torque was not applied for a single stride, to understand the immediate and time-dependent responses to missed actuations. Participants (N = 15) completed a targeted stepping task while walking with a bilateral powered ankle exoskeleton. Human-exoskeleton fluency and trust were inferred from task performance (step accuracy), step characteristics (step length and width), muscle activity, and lower limb joint kinematics. Reductions in ankle plantarflexion during catch trials suggest user adaptation to the exoskeleton. Hip flexion and muscle activity were modulated to mitigate effects of the loss of exoskeleton torque and reduced plantarflexion during catch trials to support task accuracy and maintain step characteristics. Trust was not impacted by this level of error, as there were no significant differences in task performance or gait characteristics over time. Understanding the interactions between human-exoskeleton fluency, task accuracy, and gait strategies will support exoskeleton controller development. Future work will investigate various levels of actuation reliability to understand the transition where performance and trust are affected.