Accelerating the Estimation of Metabolic Cost Using Signal Derivatives: Implications for Optimization and Evaluation of Wearable Robots

Abstract

A chief goal for lower-limb wearable robots (e.g., exoskeletons) is to augment the user's natural motion in a helpful, intuitive way. Accordingly, a common evaluation metric for such systems is the effect of the robotic assistance on the metabolic cost of the wearer. Recent hardware and control advancements have enabled researchers to achieve the challenging task of reducing the metabolic cost of walking below that of walking without an exoskeleton, using autonomous [1], [2], tethered [3], and even passive [4] devices. Although energy expenditure has long been used as an evaluation metric for device efficacy, it has also been proposed as a design specification (i.e., the Augmentation Factor [5]) and, recently, used as a physiological cost function for the realtime control of wearable robotic devices-a strategy known as <;i>body-in-the-loop<;/i> or <;i>human-in-the-loop optimization<;/i>. These algorithms use real-time estimates of metabolic cost to iteratively tune the actuation profile of an assistive device to minimize the wearer's energetic cost [6]-[10].