Person:

Ding, Ye

Background: Recent advances in wearable robotic devices have demonstrated the ability to reduce the metabolic cost of walking by assisting the ankle joint. To achieve greater gains in the future it will be important to determine optimal actuation parameters and explore the effect of assisting other joints. The aim of the present work is to investigate how the timing of hip extension assistance affects the positive mechanical power delivered by an exosuit and its effect on biological joint power and metabolic cost during loaded walking. In this study, we evaluated 4 different hip assistive profiles with different actuation timings: early-start-early-peak (ESEP), early-start-late-peak (ESLP), late-start-early-peak (LSEP), late-start-late-peak (LSLP). Methods: Eight healthy participants walked on a treadmill at a constant speed of 1.5 m · s-1 while carrying a 23 kg backpack load. We tested five different conditions: four with the assistive profiles described above and one unpowered condition where no assistance was provided. We evaluated participants’ lower limb kinetics, kinematics, metabolic cost and muscle activation. Results: The variation of timing in the hip extension assistance resulted in a different amount of mechanical power delivered to the wearer across conditions; with the ESLP condition providing a significantly higher amount of positive mechanical power (0.219 ± 0.006 W · kg-1) with respect to the other powered conditions. Biological joint power was significantly reduced at the hip (ESEP and ESLP) and at the knee (ESEP, ESLP and LSEP) with respect to the unpowered condition. Further, all assistive profiles significantly reduced the metabolic cost of walking compared to the unpowered condition by 5.7 ± 1.5 %, 8.5 ± 0.9 %, 6.3 ± 1.4 % and 7.1 ± 1.9 % (mean ± SE for ESEP, ESLP, LSEP, LSLP, respectively). Conclusions: The highest positive mechanical power delivered by the soft exosuit was reported in the ESLP condition, which showed also a significant reduction in both biological hip and knee joint power. Further, the ESLP condition had the highest average metabolic reduction among the powered conditions. Future work on autonomous hip exoskeletons may incorporate these considerations when designing effective control strategies. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0196-8) contains supplementary material, which is available to authorized users.

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).