Human-in-the-loop Bayesian optimization of wearable device parameters.

ABSTRACT: The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on human-in-the-loop optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization-a family of sample-efficient, noise-tolerant, and global optimization methods-for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work

SUBMITTER: Kim M 

PROVIDER: S-EPMC5604949 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature