Christian Klauer

A myocontrolled neuroprosthesis integrated with a passive exoskeleton to support upper limb activities

This work aimed at designing a myocontrolled arm neuroprosthesis for both assistive and rehabilitative purposes. The performance of an adaptive linear prediction filter and a high-pass filter to estimate the volitional EMG was evaluated on healthy subjects (N=10) and neurological patients (N=8) during dynamic hybrid biceps contractions. A significant effect of filter (p=0.017 for healthy; p<0.001 for patients) was obtained. The post hoc analysis revealed that for both groups only the adaptive filter was able to reliably detect the presence of a small volitional contribution. An on/off non-linear controller integrated with an exoskeleton for weight support was developed. The controller allowed the patient to activate/deactivate the stimulation intensity based on the residual EMG estimated by the adaptive filter. Two healthy subjects and 3 people with Spinal Cord Injury were asked to flex the elbow while tracking a trapezoidal target with and without myocontrolled-NMES support. Both healthy subjects and patients easily understood how to use the controller in a single session. Two patients reduced their tracking error by more than 60% with NMES support, while the last patient obtained a tracking error always comparable to the healthy subjects performance (<4°). This study proposes a reliable and feasible solution to combine NMES with voluntary effort.

Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation.

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOF. The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s.

Linearisation of electrically stimulated muscles by feedback control of the muscular recruitment measured by evoked EMG

A novel feedback control method for neuro-prosthetic systems is presented which linearises the static input non-linearity of muscles that are artificially activated by Functional Electrical Stimulation (FES). The proposed method controls the activation state (sum of recruited motor units by FES) of the paralysed muscle. This muscle recruitment state is measured by the FES evoked electromyogram (eEMG). Compared to standard control approaches, no cumbersome off-line or online identification of the complete nonlinear muscle dynamics, often described by Hammerstein or Hill models, and no model inversion are required. The developed approach robustly handles uncertainties and time-variances of the highly nonlinear muscular recruitment behaviour. When building motion control systems for neuro-prostheses, the developed feedback controller should be used at an inner loop receiving command signals (desired muscle activation) from a top level joint-angle control loop. The feasibility of the proposed control scheme has been experimentally demonstrated for the control of the elbow-joint angle in healthy subjects.

EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI-FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.

An EMG-controlled neuroprosthesis for daily upper limb support: A preliminary study

MUNDUS is an assistive platform for recovering direct interaction capability of severely impaired people based on upper limb motor functions. Its main concept is to exploit any residual control of the end-user, thus being suitable for long term utilization in daily activities. MUNDUS integrates multimodal information (EMG, eye tracking, brain computer interface) to control different actuators, such as a passive exoskeleton for weight relief, a neuroprosthesis for arm motion and small motors for grasping. Within this project, the present work integreted a commercial passive exoskeleton with an EMG-controlled neuroprosthesis for supporting hand-to-mouth movements. Being the stimulated muscle the same from which the EMG was measured, first it was necessary to develop an appropriate digital filter to separate the volitional EMG and the stimulation response. Then, a control method aimed at exploiting as much as possible the residual motor control of the end-user was designed. The controller provided a stimulation intensity proportional to the volitional EMG. An experimental protocol was defined to validate the filter and the controller operation on one healthy volunteer. The subject was asked to perform a sequence of hand-to-mouth movements holding different loads. The movements were supported by both the exoskeleton and the neuroprosthesis. The filter was able to detect an increase of the volitional EMG as the weight held by the subject increased. Thus, a higher stimulation intensity was provided in order to support a more intense exercise. The study demonstrated the feasibility of an EMG-controlled neuroprosthesis for daily upper limb support on healthy subjects, providing a first step forward towards the development of the final MUNDUS platform.

Compensating the effects of FES-induced muscle fatigue by rehabilitation robotics during arm weight support

Abstract: Motor functions can be hindered in consequence to a stroke or a spinal cord injury. This often results in partial paralyses of the upper limb. The effectiveness of rehabilitation therapy can be improved by the use of rehabilitation robotics and Functional Electrical Stimulation (FES). We consider a hybrid arm weight support combining both. In order to compensate the effect of FES-induced muscle fatigue, we introduce a method to substitute the decreasing level of FES support by cable-driven robotics. We evaluated the approach in a trial with one healthy subject performing repetitive arm lifting. The controller automatically adapted the support and thus no increase in user generated volitional effort was observed when FES induced muscle fatigue occured.

A muscle model for hybrid muscle activation

Abstract 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.

Gelenkwinkelregelung durch Elektrostimulation eines antagonistischen Muskelpaares

Zusammenfassung 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. Abstract This contribution presents a new approach to human arm position control under electrical stimulation that employs an acceleration feedback. During stimulation of antagonistic muscle pairs, this approach can noticeably reduce negative effects that are caused by an uncertain dead-zone within the muscular activation. The acceleration controller, realised using a disturbance observer, represents the lowest level of a cascaded control scheme. On higher levels the angle and the velocity are controlled. The overall control design is based on a highly simplified neuro-muscular model, which can be easily adapted to different subjects.

Enhancing the smoothness of joint motion induced by functional electrical stimulation using co-activation strategies

Abstract The motor precision of today’s neuroprosthetic devices that use artificial generation of limb motion using Functional Electrical Stimulation (FES) is generally low. We investigate the adoption of natural co-activation strategies as present in antagonistic muscle pairs aiming to improve motor precision produced by FES. In a test in which artificial knee-joint movements were generated, we could improve the smoothness of FES-induced motion by 513% when applying co-activation during the phases in which torque production is switched between muscles – compared to no co-activation. We further demonstrated how the co-activation level influences the joint stiffness in a pendulum test.

Discretisation & control of irregularly actuated and sampled LTI systems

Motivated by control tasks in neuro-prosthetic systems, a method for the exact discretisation of continuous-time LTI systems in presence of irregular actuation and sampling times is presented. In this approach, sequences for the desired sampling and actuation times form additional inputs to the discrete-time systems. An analytical evaluation for a class of practically relevant systems is presented that yields discrete-time state-space systems with non-constant coefficients. Because of the non-linearity introduced by the irregular time intervals, a linearising controller is proposed that allows to re-use standard linear controllers. A simulation example demonstrates the feasibility of the presented control approach for a neuro-prosthetic system.