"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost

PLoS One. 2015 Aug 19;10(8):e0135342. doi: 10.1371/journal.pone.0135342. eCollection 2015.

Abstract

This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body "in-the-loop") and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects' preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Humans
  • Orthotic Devices*
  • Prostheses and Implants*
  • Rehabilitation Research
  • Robotics / instrumentation
  • Robotics / methods*
  • Self-Help Devices
  • Walking / physiology

Grant support

Funding for the work of the RAMlab came through the MCubed program at the University of Michigan (CDR) (http://mcubed.umich.edu/projects/reducing-effort-through-augmented-lower-limb-prostheses). Funding for the work done by the Locomotion Lab was supported by: U.S. Army Research Office Grant # W911NF-13-1-0268 (JMD) (www.aro.army.mil), Natural Sciences and Engineering Research Council of Canada, Discovery Grant (JMD), and Vanier Canadian Graduate Scholarship (JCS) (http://www.nserc-crsng.gc.ca/). This material is based upon work supported by the National Science Foundation Graduate Research Fellowship (WF) under Grant No. DGE 1256260. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (www.nsf.gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.