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

Myunghee Kim, Ye Ding, Philippe Malcolm, Jozefien Speeckaert, Christoper J. Siviy, Conor J. Walsh, Scott Kuindersma

Research output: Contribution to journalArticle

22 Scopus citations

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, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes, p < 0.01), smaller inter-subject variability in convergence time (± 2 minutes, p < 0.01), and lower overall energy expenditure (p < 0.01).

Original languageEnglish (US)
Article numbere0184054
JournalPloS one
Volume12
Issue number9
DOIs
StatePublished - Sep 2017

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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    Kim, M., Ding, Y., Malcolm, P., Speeckaert, J., Siviy, C. J., Walsh, C. J., & Kuindersma, S. (2017). Human-in-the-loop Bayesian optimization of wearable device parameters. PloS one, 12(9), [e0184054]. https://doi.org/10.1371/journal.pone.0184054