Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue

Abstract

The use of iterative learning control to regulate assistive functional electrical stimulation applied to the muscles of patients undergoing robotic-assisted upper limb stroke rehabilitation has been followed through to small scale clinical trials. These trials confirmed that an increase in patient ability to complete the specified task also led to a reduction in the level of electrical stimulation required. This previous work assumed that the effects of muscle fatigue could be neglected but if a patient suffers fatigue during a rehabilitation session then their the session goals are not achieved or, more likely, the session must be abandoned due to the time limits imposed by the ethical approval required to conduct such sessions. In this paper the results of the first investigation into enhancing the control scheme to remove or lessen the effects of fatigue and hence make better use of the time available for a session are given. The scheme considered adds a feedback loop around the muscle model used, where the performance results given are based on a model for the dynamics constructed using patient data collected in previous clinical trials.

Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

“…The same setup as in [10] is considered and starts with The position of the human arm can be described by Φ = ϑ a ϑ b ϑ c ϑ d ϑ e . Using a Lagrangian approach, the differential equations describing the dynamics of the support and arm can be described as…”

“…The torque provided by the muscle will decrease over time when the applied FES is constant. Previous research [10] used a time dependent and iteration dependent fatigue model f (t, k) given by τ i = (1 − k f t)λ k−1 τ m,i in which k f and λ are constants determining the time and iteration fatigue rates respectively. The model implies that the muscle will fatigue during a trial even if no input is applied and the arm does not move, which is counter-intuitive.…”

“…The task for the patient considered in [10] is that of lifting the affected arm and then reaching out from the elbow. It is well known that stroke patients experience great difficulty in lifting the affected arm and hence part of this robot compensates for gravity.…”

“…One approach to overcoming, or at least reducing, the effects of fatigue was considered in [10], which introduced a representation for the effects of fatigue into the model for the response of the muscle to applied FES with a compensating feedback loop around the model used. The results of a detailed simulation based evaluation of the new design is given where the dynamic model is constructed from data collected from patients participating in a previous clinical study of this ILC application.…”

Parameter estimation of the fractional‐order Hammerstein–Wiener model using simplified refined instrumental variable fractional‐order continuous time

“…There are several practical applications of the Hammerstein and Wiener formulations which are used to model systems with significant non-linearity in different fields. The Hammerstein model, for example, is employed in robotic therapy for describing the isometric recruitment curve, that is, the static gain relation between the stimulus activation level and steady-state output torque [23]. In the automotive industry, the battery impedance model is enhanced by introducing a Wiener static non-linearity to the ordinary equivalent circuit model [24].…”