Review of control strategies for lower-limb exoskeletons to assist gait

 But we need to know which exoskeleton gets you 100% recovered. This does little for that goal.

Review of control strategies for lower-limb exoskeletons to assist gait

• Romain Baud,
• Ali Reza Manzoori,
• Auke Ijspeert &
• Mohamed Bouri 

Journal of NeuroEngineering and Rehabilitation volume 18, Article number: 119 (2021) Cite this article

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Abstract

Background

Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user.

Methods

In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller.

Results

291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper.

Conclusions

By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

Introduction

Powered lower-limb orthotic devices, also called powered exoskeletons, are often considered as tools in rehabilitation and the assistance of the human gait. A significant amount of research in different fields has been dedicated to developing and improving the performance of these devices, and there are many challenges in this area of research due to inherent requirements of portability and safe interaction with the user and the environment. One of the most important aspects for improving the performance of these devices is their control [1].

Currently, there are two main types of exoskeletons for gait assistance: the ones for full mobilization, and the ones for partial assistance. Full mobilization exoskeletons are designed to move the legs of people suffering from a severe loss of motor control or motor disorders, typically in people with spinal cord injury SCI. The actuators must have a high torque capability because they provide the entire torque required for the movement. Such devices are available commercially since 2011, when the ReWalk (ReWalk Robotics, Israel) was released on the market. They could be developed quickly because their control strategy can be simply position control over time. There is no need to collaborate with an existing voluntary movement of the legs, because there is none (or it is very weak) and thus the user’s legs are assumed to be passive. The start of the gait is often triggered by the upper body movements or buttons pressed by the fingers, which is simple to implement. These exoskeletons seem more successful because they dramatically improve the bipedal ambulation capability (from no gait at all to some slow gait).

Partial assistance devices are generally lighter, targeting various less severe handicaps. These could be the loss of stamina because of aging [2], the loss of strength or coordination because of incomplete spinal cord injury SCI, stroke, neurodegenerative diseases, etc. These devices can also assist the gait of healthy people, which can be useful for endurance augmentation purposes. This is more challenging because the device has to assist more than it is hindering its user, given the complex nature of the interaction with the user. People who can already walk independently also have higher expectations for the performance (e.g. higher gait speed). A major subcategory of partial assistance exoskeletons are the devices that are intended for rehabilitation purposes.^{Footnote 1} Here, the ultimate purpose is to train the users to become independent of the assistance offered by the device. A fundamental distinction can thus be made between the desired outcomes of these exoskeletons versus the ones that are used to directly assist the mobility only when wearing the device. Actually, a training strategy for rehabilitation may consist in resisting the user movement [3]. Notwithstanding this difference in the end goals, there is a lot of commonality between the two applications in terms of the techniques used for control.

Several reviews already exist on different aspects of exoskeletons and gait assistance devices, but very few are focused on control. The two most exhaustive reviews of control strategies to date are the ones of Tucker et al. [4] and Yan et al. [5]. However, these are already 5 years old at the time of writing this paper, and many new developments deserve to be mentioned, since this field is evolving fast. More than 190 new publications addressing control strategies have been identified since the publication of the two previous reviews in 2015, and advancements have been made with new control methods and device designs, resulting in major performance improvements in terms of metrics such as metabolic cost reduction and capabilities such as crutch-less dynamic walking. The review of Tucker et al. is broad and considered both orthoses and prostheses. A “generalized control framework” was proposed with a 3-layer hierarchical controller, and also the environment, the user and the hardware of the device. But this review did not provide much detail on the mid-level layer of control. The article by Yan et al. focuses on the control of exoskeletons and orthoses, but it is mostly organized around the devices themselves, and how they are built (e.g. single/multi joints).

Some reviews have also been recently published on gait assistance devices [6,7,8,9], but none of them comprehensively address the control aspect. A recent review by Sawicki et al. [7] focused on comparing the results of partial assistance for the gait, and only considered the successful orthoses with respect to metabolic cost reduction. This excludes all the devices that did not undergo such testing and also full mobilization exoskeletons. Also in this article, few details are given on the details of the control part. A more broad review by Kalita et al. [8] studied the existing exoskeletons and orthoses in the literature, categorizing them according to joint structure, actuation and control strategy. Control strategies are roughly divided into 9 categories, each one only briefly explained without going into the details.

In this review, the various control approaches of gait assistance devices are thoroughly addressed, focusing on the lower-limb exoskeletons designed to enhance the locomotion of disabled or healthy people. Compared to the existing reviews, a stronger emphasis is placed on the control methods and separating them from the hardware and implementation details as much as possible. Based on the existing control methods in the literature, a modular classification framework consisting of 3 layers is proposed. The purpose of the framework is to enable describing all of the existing control strategies with the minimum number of functional elements. This paper also shortly reviews the metrics used to characterize the performance of these robots when worn by a user. However, the assessment of the performance of the cited controllers and their comparison are beyond the scope of this review.

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