  External feedback in the form of visual, auditory and tactile cues has been
used to assist patients to overcome mobility challenges. However, these cues
can become less effective over time. There is limited research on adapting cues
to account for inter and intra-personal variations in cue responsiveness. We
propose a cue-provision framework that consists of a gait performance
monitoring algorithm and an adaptive cueing strategy to improve gait
performance. The proposed approach learns a model of the person's response to
cues using Gaussian Process regression. The model is then used within an
on-line optimization algorithm to generate cues to improve gait performance. We
conduct a study with healthy participants to evaluate the ability of the
adaptive cueing strategy to influence human gait, and compare its effectiveness
to two other cueing approaches: the standard fixed cue approach and a
proportional cue approach. The results show that adaptive cueing is more
effective in changing the person's gait state once the response model is
learned compared to the other methods.
