Design of the Cooperative Actuation in Hybrid Orthoses: A Theoretical Approach Based on Muscle Models

Abstract

Hybrid orthoses or rehabilitation exoskeletons have proven to be a powerful tool for subjects with gait disabilities due to their combined use of electromechanical actuation to provide motion and support, and functional electrical stimulation (FES) to contract muscle tissue so as to improve the rehabilitation process. In these devices, each degree of freedom is governed by two actuators. The main issue arises in the design of the two actuation profiles for there to be natural or normative gait motion in which the two actuators are transparent to each other. Hybrid exoskeleton control solutions proposed in the literature have been based on tracking the desired kinematics and applying FES to maintain the desired motion rather than to attain the values expected for physiological movement. This work proposes a muscle-model approach involving inverse dynamics optimization for the design of combined actuation in hybrid orthoses. The FES profile calculated in this way has the neurophysiological meaningfulness for the device to be able to fulfill its rehabilitative purpose. A general scheme is proposed for a hybrid hip-knee-ankle-foot orthosis. The actuation profiles, when muscle tissue is fatigued due to FES actuation are analyzed, and an integrated approach is presented for estimating the actuation profiles so as to overcome muscle peak force reduction during stimulation. The objective is to provide a stimulation profile for each muscle individually that is compatible with the desired kinematics and actuation of the orthosis. The hope is that the results may contribute to the design of subject-specific rehabilitation routines with hybrid exoskeletons, improving the exoskeleton's actuation while maintaining its rehabilitative function.

Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study

“…However, for tasks involving high intensities where loss of muscle force may be expected, the ability to combine muscle fatigue models with muscle force and load-sharing paradigms is increasingly important. These type of simulations may be useful for functional electrical stimulation (FES) ( Romero-Sánchez et al, 2019 ), motor control and prediction ( Thelen et al, 2003 ), or ergonomic applications in which estimates of muscle force over time is relevant, such as may be needed for rehabilitation, prevention of injuries in sports or workplaces, or even surgical planning to reconstruct diseased joints.…”

Model-Based Shared Control of a Hybrid FES-Exoskeleton: An Application in Participant-Specific Robotic Rehabilitation

“…However, the motions are externally guided, and the exercise is often ineffective unless patients are actively engaged [3]. Hybrid exoskeleton can overcome the drawbacks of these actuators [4]- [6], and it is a promising approach to improve the recovery of human motor functions without introducing excessive muscle fatigue [7], [8]. However, combining FES and exoskeleton for assisting limb movement poses various challenges.…”

“…From control perspectives, for instance, there is an actuation redundancy problem [9]- [11], such that the effect of human volitional activities must be considered in the control loop [12], as well as the coordination between the exoskeleton and FES actuations. Furthermore, FES is directly interfaced with the user, and participant-specific behavior must be considered at the neuromachanical level for control performance [8], [13].…”

“…Various theoretically and empirically motivated hybrid exoskeleton control methods address these challenges [8], [11]. [8], for example, presents a theoretical cooperative control approach based on muscle-model using inverse dynamics optimization to allocate the control effort between the two actuators in the lower-limb motion.…”

Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study

“…This algorithm is designed to keep the contribution from neural stimulation at a fixed amount and only updates motor contributions over time. The study described in [ 11 ] reports a similar method of control with an inverse muscle model. It implemented an extended optimization for joint torque distribution over multiple redundant agonist muscles and actuators and factored in an internal estimate of muscle fatigue.…”