Human-in-the-loop optimization of exoskeleton assistance during walking

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Science  23 Jun 2017:
Vol. 356, Issue 6344, pp. 1280-1284
DOI: 10.1126/science.aal5054
  • Fig. 1 Human-in-the-loop optimization.

    (A) Measurements of human performance are used to update device control so as to improve performance in the human portion of the system. (B) A method for minimizing the energy cost of human walking, in which various control laws are applied, metabolic (met.) rate is quickly estimated (est.) for each, costs are compared, and an evolution strategy is used to generate a new set of control laws to be tested, all during walking. p1 and p2 are hypothetical control parameters.

  • Fig. 2 Control law and exoskeleton hardware.

    (A) Parameterization of ankle torque. Each control law determined applied torque as a function of time, normalized to stride period, as a cubic spline defined by peak time, rise time, fall time, and peak torque. (B) Examples of possible torque patterns. (C) Exoskeleton emulator system. Off-board motor and control hardware actuated a tethered exoskeleton worn on one ankle while participants walked on a treadmill. (D) Ankle exoskeleton. Drive rope tension caused the device to push on the shank, heel, and toe contact, generating an ankle torque. High-quality images are shown in figs. S2 and S3.

  • Fig. 3 Experimental results.

    (A) Metabolic energy cost of walking for each condition, tested in validation trials. Optimized assistance resulted in the lowest metabolic rate and a large reduction compared to the zero-torque condition. Variability is primarily due to differences between participants. Bars are means, error bars are standard deviations, and asterisks denote statistical significance (P < 0.05). (B) Results from a prior experiment using the same hardware (17), comparing the zero-torque condition with walking in normal shoes (no exoskeleton) or with static assistance. Static assistance provided a smaller benefit in the prior study. Bars are means, and error bars are standard deviations. (C) Optimized control law parameter values. Optimized values varied widely across participants. Values are normalized to their allowable range (26). Lines are medians, boxes cover the 25th to 75th percentiles, and whiskers show the range. (D) Optimized ankle exoskeleton torque pattern for each participant. Patterns varied widely and spanned a large portion of the allowable range. Lines are measured torque, normalized to stride time and body mass, averaged across strides. (E) Torque applied in the static and zero-torque conditions. The static pattern, based on (17), is similar to the optimized patterns but resulted in higher metabolic rate. Torque was negligible in the zero-torque mode. Lines are measured torque, normalized to stride time and body mass, averaged across strides and participants.

  • Fig. 4 Single-subject studies under alternate conditions.

    (A) Slow walking (0.75 m s−1). (B) Normal walking (1.25 m s−1). (C) Fast walking (1.75 m s−1). (D) Uphill walking (10% grade). (E) Loaded walking (load equal to 20% of body mass). (F) Running (2.68 m s−1). All speeds, grades, loads, and gaits were with bilateral ankle exoskeletons. (G) Optimizing soleus muscle activity, rather than metabolic rate, during normal-speed walking. In each case, one participant was tested (n = 1). The method identified assistance patterns that substantially improved the target outcome in all circumstances. Optimized torque patterns can be found in figs. S6 to S8.

Supplementary Materials

  • Human-in-the-loop optimization of exoskeleton assistance during walking

    Juanjuan Zhang, Pieter Fiers, Kirby A. Witte, Rachel W. Jackson, Katherine L. Poggensee, Christopher G. Atkeson, Steven H. Collins

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    •  Materials and Methods 
    • Figs. S1 to S11 
    • Tables S1 to S4 
    • Captions for Data S1 and S2 
    • References 

    Additional Data

    Data S1
    Complete study data are provided in the file archive Data S1. Data from both the optimization and validation phases of both the main experiment and single-subject studies are found in the Matlab archives MainStudy.mat and SingleSubjectStudies.mat. Optimization data include subject, generation number, generation mean, trial day, control law parameter values, net mechanical power, root mean square muscle activity, and estimated steady-state metabolic rate, as appropriate, for each control law tested. Validation data include subject, condition name, control law parameter values, net mechanical power, average measured exoskeleton torque trajectory, muscle activity trajectory, root mean square muscle activity, and estimated steady-state metabolic rate, as appropriate, for each condition tested during validation. The readme.txt file provides a more detailed description of the file structure and examples of how to access and process data. Matlab code in the sample_processing.m file demonstrates how to access and process study data, including recreating all outcomes and figures from the main study and single-subject studies reported in the main text and supplementary materials
    Data S2
    Sample versions of the code that comprise the optimization method are provided in the file archive Data S2. Included are sample scripts for applying parametric constraints and generating torque curves, estimating steady-state metabolic rate from breath by breath data from each control law, and applying the CMA-ES algorithm to determine the next generation of control laws to test. Code is provided in Matlab format, with instructions and descriptions as comments within each file.

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