Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton

Abstract

BackgroundRobotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user’s muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user’s myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain.MethodsWe tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user’s peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms-1. We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics.ResultsUsing our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power.ConclusionsOur findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-015-0086-5) contains supplementary material, which is available to authorized users.

A unified perspective on ankle push-off in human walking

“…Likewise, increased hip powering (pulling the limb into swing) may reduce ankle push-off demands. There is empirical evidence in support of both of these contentions: increased ankle push-off reducing hip work (Caputo and Collins, 2014;Koller et al, 2015;Lewis and Ferris, 2008) and increased hip powering reducing ankle push-off (Lenzi et al, 2013).…”

Muscle recruitment and coordination with an ankle exoskeleton

“…Prior work has demonstrated that exoskeletons can reduce demand, and hence activity-level, of individual muscles and muscle groups. In particular, both passive and powered ankle exoskeletons have been shown to reduce ankle plantarflexor demand, both with and without myoelectric control (Collins et al, 2015; Ferris et al, 2005b; Koller et al, 2015). However, exoskeleton assistance does not necessarily lead to reductions in muscle activity.…”

A Biomechanical Comparison of Proportional Electromyography Control to Biological Torque Control Using a Powered Hip Exoskeleton

“…Controller studies on testbed ankle exoskeleton systems (Ferris et al, 2006; Sawicki and Ferris, 2008; Malcolm et al, 2013; Farris et al, 2014b; Galle et al, 2015; Jackson and Collins, 2015; Kim et al, 2015; Koller et al, 2015a; Sawicki and Khan, 2015; Takahashi et al, 2015) and multi-joint exoskeleton systems (Neuhaus et al, 2011; Van Kammen et al, 2014; Cestari et al, 2015; Wang et al, 2015; van Asseldonk and van der Kooij, 2016; Stroppa et al, 2017) have helped enable the development of highly engineered, autonomous ankle (Meijneke et al, 2014; Mooney et al, 2014; Collins et al, 2015; Mooney and Herr, 2016; van Dijk et al, 2017) and multi-joint exoskeletons (Zoss et al, 2006; Sasaki et al, 2013; Hartigan et al, 2015; Kozlowski et al, 2015; Raab et al, 2016; Grasmücke et al, 2017) with promising results. Similarly, with hip exoskeletons, there is a need for testbed system results (Lewis and Ferris, 2011; Lenzi et al, 2013; Young et al, 2017) to help inform the control of new autonomous systems (Giovacchini et al, 2014; Buesing et al, 2015; Seo et al, 2016; Karavas et al, 2017; Sugar et al, 2017).…”

“…In the limited studies on hip exoskeletons, human users tend to reduce their own muscle joint torque and allow the robotic torque to substitute (Ding et al, 2017), as seen with ankle exoskeletons (Lewis and Ferris, 2011). In addition, there appears to be a trade-off between hip and ankle mechanical power in that both joints can compensate for each other during push-off (Lewis and Ferris, 2008; Lenzi et al, 2013; Koller et al, 2015a). Further testing to optimize hip exoskeleton controllers is needed to empower autonomous hip exoskeleton systems.…”