Automatic Control of a Drop‐Foot Stimulator Based on Angle Measurement Using Bioimpedance

Abstract

The topic of this contribution is iterative learning control of a drop-foot stimulator in which a predefined angle profile during the swing phase is realized. Ineffective dorsiflexion is compensated by feedback-controlled stimulation of the muscle tibialis anterior. The ankle joint measurement is based on changes in the bioimpedance (BI) caused by leg movements. A customized four-channel BI measurement system was developed. The suggested control approach and the new measurement method for the joint angle were successfully tested in preliminary experiments with a neurologically intact subject. Reference angle measurements were taken with a marker-based optical system. An almost linear relation between joint angle and BI was found for the angle range applicable during gait. The desired angle trajectory was closely tracked by the iterative learning controller after three gait cycles. The final root mean square tracking error was below 5 degrees.

Iterative learning control of drop foot stimulation with array electrodes for selective muscle activation

“…While the control methods used therein yield sufficient foot clearance, they do not guarantee a natural foot motion in form of a desired dor-siflexion angle trajectory. That this additional objective can be achieved by the use of Iterative Learning Control (ILC) was demonstrated in (Nahrstaedt et al, 2008;Seel et al, 2013c) through simplified experiments with healthy subjects and in (Seel et al, 2013a) with stroke patients walking on a treadmill.…”

“…The former can be determined using an inertial measurement unit 1 on the foot (Negard, 2009). In contrast, determining the joint angle requires a second IMU on the shank (Seel et al, 2013a), or alternatively a goniometer (Mourselas and Granat, 2000) or bioimpedance measurements (Nahrstaedt et al, 2008). In case inertial sensors are used, the heel switch can be replaced by a gait phase detection that uses the measured accelerations and angular rates instead (Kotiadis et al, 2010;Negard, 2009;Seel et al, 2013b;Héliot et al, 2013).…”

Iterative Learning Control for Variable Pass Length Systems

“…For this purpose, controller design and simulations are carried out for a simple example application system that is motivated by a recent publication by Nahrstaedt et al [9]. Therein, functional electrical stimulation of the tibialis anterior muscle is used to produce a predefined foot movement during the swing phase of gait (i.e.…”

“…Due to the repetitive nature of gait, ILC was successfully used to learn the stimulation profile for the swing phase within a small number of steps, see [9]. However, the hip joint and the knee joint are actuated by the patients themselves.…”

“…Typically, this problem is either ignored completely, or a heuristic approach is used hoping for convergence to be maintained (see e.g. [9], [8]). But currently there is no ILC theory that guarantees stability or monotonic convergence for such variable pass length systems.…”