Dieser Beitrag beschreibt einen neuen Ansatz zur kaskadierten Positionsregelung menschlicher Extremitäten unter Elektrostimulation, welcher auch eine Rückführung der Beschleunigung in Form eines Störgrößenbeobachters verwendet. Bei der Stimulation eines antagonistischen Muskelpaares kann dieser Regelungsansatz unerwünschte Effekte, die durch eine nicht exakt bekannte Totzone in der muskulären Aktivierung hervorgerufen werden, deutlich reduzieren. Der Reglerentwurf basiert auf einem stark vereinfachten neuro-muskulären Modell, das mit geringem Aufwand an den Probanden angepasst werden kann.
To develop model-based control strategies for Functional Electrical Stimulation (FES) in order to support weak voluntary muscle contractions, a hybrid model for describing joint motions induced by concurrent voluntary-and FES induced muscle activation is proposed. It is based on a Hammerstein model – as commonly used in feedback controlled FES – and exemplarily applied to describe the shoulder abduction joint angle. Main component of a Hammerstein muscle model is usually a static input nonlinearity depending on the stimulation intensity. To additionally incorporate voluntary contributions, we extended the static non-linearity by a second input describing the intensity of the voluntary contribution that is estimated by electromyography (EMG) measurements – even during active FES. An Artificial Neural Network (ANN) is used to describe the static input non-linearity. The output of the ANN drives a second-order linear dynamical system that describes the combined muscle activation and joint angle dynamics. The tunable parameters are adapted to the individual subject by a system identification approach using previously recorded I/O-data. The model has been validated in two healthy subjects yielding RMS values for the joint angle error of 3.56° and 3.44°, respectively.
Functional electrical stimulation leg cycle ergometry (FES-LCE), which is often used as exercise for people with spinal cord injury (SCI), has recently been applied in the motor rehabilitation of stroke patients. Recently completed studies show controversial results, but with a tendency to positive training effects. Current technology is identical to that used in FES-LCE for SCI, whereas the pathology of stroke differs strongly. Most stroke patients with hemiparesis are able to drive an ergometer independently. Depending on the degree of spasticity, the paretic leg will partially support or hinder movements. Electrical stimulation increases muscle force and endurance and both are prerequisites for restoring gait. However, the effect of FES-LCE on improving impaired motor coordination is unclear. To measure motor coordination during FES-LCE, an EMG-amplifier design has been investigated which suppresses stimulation artifacts and allows detection of volitional or reflex induced muscle activity. Direct measurement of EMG from stimulation electrodes between stimulation pulses is an important asset of this amplifier. Photo-MOS switches in front of the preamplifier are utilized to achieve this. The technology presented here can be used to monitor the effects of FES-LCE to adapt the stimulation strategy or to realize EMG-biofeedback training.
Dieser Beitrag beschreibt den Entwurf einer linearen Iterativ Lernenden Regelung (ILR) im Zeitbereich für die automatische Anpassung einer Neuroprothese bei Schlaganfallpatienten. Eine unzureichende Fußhebung in der Schwungphase des Ganges soll durch die gezielte Stimulation des Fußhebers kompensiert werden. Für die Erfassung des Gelenkwinkels wird ein neuartiges Messverfahren verwendet, bei welchem durch Bewegung hervorgerufene Bio-Impedanz-Änderungen im Bein registriert werden. Für die Regelung ist ferner eine Gangphasenerkennung mittels Drucksensoren unter der Fußsohle notwendig, um die Stimulation exakt mit der Schwungphase zu synchronisieren. Ein erster Test des Regelungskonzepts und des neuen Ansatzes zur Winkelmessung wurde an einem gesunden Probanden durchgeführt.
Inertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.
Foot orientation can be assessed in realtime by means of a foot-mounted inertial sensor. We consider a method that uses only accelerometer and gyroscope readings to calculate the foot pitch and roll angle, i.e. the foot orientation angle in the sagittal and frontal plane, respectively. Since magnetometers are avoided completely, the method can be used indoors as well as in the proximity of ferromagnetic material and magnetic disturbances. Furthermore, we allow for almost arbitrary mounting orientation in the sense that we only assume one of the local IMU coordinate axes to lie in the sagittal plane of the foot. The method is validated with respect to a conventional optical motion capture system in trials with transfemoral amputees walking with shoes and healthy subjects walking barefoot, both at different velocities. Root mean square deviations of less than 4° are found in all scenarios, while values near 2° are found in slow shoe walking. This demonstrates that the proposed method is suitable for realtime application such as the control of FES-based gait neuroprostheses and active orthoses.
Stroke and other neurological disorders often lead to reduced motor function and to pathological foot motion during gait. We consider Functional Electrical Stimulation (FES) of the shank muscles that control dorsiflexion (related to pitch) and eversion (related to roll) of the foot. We describe the nonlinear domain of stimulation intensities that are tolerated by subjects in combined two-channel FES via surface electrodes. Two piecewise linear parameterizations of this domain are suggested and compared in terms of the cross-couplings between the newly defined stimulation intensity coordinates and the foot motion caused during swing phase in drop foot patients walking on a treadmill. Both parameterizations are found to yield almost monotonous input-output behavior and therefore facilitate decentralized control of the foot pitch and roll angle.