Confidence in the curve: Establishing instantaneous cost mapping techniques using bilateral ankle exoskeletons

Abstract

Lower extremity robotic prostheses and exoskeletons can require tuning a large number of control parameters on a subject-specific basis to reduce users’ metabolic power during locomotion. We refer to the functional relationship between control parameter configurations and users’ metabolic power as the metabolic cost landscape. Standard practice for estimating a metabolic cost landscape, and thus identifying optimal parameter configurations, is to vary control parameters while measuring steady-state metabolic power during walking. This approach is time consuming, tedious, and inefficient. We have developed an instantaneous cost mapping analysis that allows for an estimate of the metabolic cost landscape without the explicit need for steady-state measurements. Here we present novel methods to quantify the confidence in an estimated metabolic cost landscape, allowing for an objective subject-specific comparison of protocols regardless of which metabolic analysis is used. We validated these techniques by estimating metabolic cost landscapes for healthy subjects walking with bilateral robotic ankle exoskeletons using a standard practice protocol and two innovative protocols that use an instantaneous cost mapping analysis. All cost landscapes were a function of the devices’ actuation timing. Results showed that for this device a protocol using an instantaneous cost mapping analysis could accurately identify optimal parameter configurations in 20 min, where the standard practice protocol required 42 min. Additionally, using an instantaneous cost mapping analysis with the standard practice’s parameter exploration significantly improved fit confidence. These methods could greatly improve real-time optimization of robotic assistive devices or studies focused on biomechanical manipulations of locomotion. NEW & NOTEWORTHY We are presenting novel subject-specific metabolic cost landscape confidence analyses. These confidence analyses can greatly improve experimental design, intersubject analysis, and the comparison of landscape mapping protocols. We validated these methods by mapping subject-specific metabolic cost landscapes using bilateral ankle exoskeletons and are presenting the first full study using instantaneous cost mapping techniques to optimally tune an assistive robotic device.