Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis

Robot-Inspired Human Impedance Control Through Functional Electrical Stimulation

Functional Electrical Stimulation is an effective tool to foster rehabilitation of neurological patients suffering from impaired motor functions. It can also serve as an assistive device to compensate for compromised motor functions in the chronic phase occurring after a disease or trauma. In all cases, the dominant paradigm in FES applications is that of aiding specialized, task-specific movements, such as reaching or grasping. Usually this is achieved by targeting specific muscle groups which are associated to the targeted motion by experts. A general purpose, FES-based control theory capable of enabling neurological patients to achieve a wide range of positional goals in their peri-personal space is still missing. In this paper, we present an early analysis of the performance achievable through a muscular impedance control loop employing FES to actuate force and movement. The control is evaluated in a test where the user's upper limb is moved by means of an exonerve to a series of target positions on a plane without providing visual feedback nor requiring volitional effort. The results allow to characterize the performance of such a setup over time and to assess how well can it generalize over different target positions in the user's peri-personal space. The current study population also allows to evaluate the effects of user's experience with FES systems on the overall performance during the test. The results indicate that the proposed control loop can generalize well over different arm poses.

Visual Feedback Control of a Rat Ankle Angle Using a Wirelessly Powered Two-Channel Neurostimulator

Peripheral nerve disconnections cause severe muscle atrophy and consequently, paralysis of limbs. Reinnervation of denervated muscle by transplanting motor neurons and applying Functional Electrical Stimulation (FES) onto peripheral nerves is an important procedure for preventing irreversible degeneration of muscle tissues. After the reinnervation of denervated muscles, multiple peripheral nerves should be stimulated independently to control joint motion and reconstruct functional movements of limbs by the FES. In this study, a wirelessly powered two-channel neurostimulator was developed with the purpose of applying selective FES to two peripheral nerves—the peroneal nerve and the tibial nerve in a rat. The neurostimulator was designed in such a way that power could be supplied wirelessly, from a transmitter coil to a receiver coil. The receiver coil was connected, in turn, to the peroneal and tibial nerves in the rat. The receiver circuit had a low pass filter to allow detection of the frequency of the transmitter signal. The stimulation of the nerves was switched according to the frequency of the transmitter signal. Dorsal/plantar flexion of the rat ankle joint was selectively induced by the developed neurostimulator. The rat ankle joint angle was controlled by changing the stimulation electrode and the stimulation current, based on the Proportional Integral (PI) control method using a visual feedback control system. This study was aimed at controlling the leg motion by stimulating the peripheral nerves using the neurostimulator.

Predicting functional force production capabilities of upper extremity functional electrical stimulation neuroprostheses: a proof of concept study

OBJECTIVE

This study's goal was to demonstrate person-specific predictions of the force production capabilities of a paralyzed arm when actuated with a functional electrical stimulation (FES) neuroprosthesis. These predictions allow us to determine, for each hand position in a person's workspace, if FES activated muscles can produce enough force to hold the arm against gravity and other passive forces, the amount of force the arm can potentially exert on external objects, and in which directions FES can move the arm.

APPROACH

We computed force production predictions for a person with high tetraplegia and an FES neuroprosthesis used to activate muscles in her shoulder and arm. We developed Gaussian process regression models of the force produced at the end of the forearm when stimulating individual muscles at different wrist positions in the person's workspace. For any given wrist position, we predicted all possible forces a person can produce by any combination of individual muscles. Based on the force predictions, we determined if FES could produce force sufficient to overcome passive forces to hold a wrist position, the maximum force FES could produce in all directions, and the set of directions in which FES could move the arm. To estimate the error in our predictions, we then compared our force predictions based on single-muscle models to the actual forces produced when stimulating combinations of the person's muscles.

MAIN RESULTS

Our models classified the person's ability to hold static arm positions correctly for 83% (Session #1) and 69% (Session #2) for 39 wrist positions over two sessions. We predicted this person's ability to produce force at the end of her arm with an RMS error of 5.5 N and the percent of directions for which FES could achieve motion with RMS error of 10%. The accuracy of these predictions is similar to that found in the literature for FES systems with fewer degrees of freedom and fewer muscles.

SIGNIFICANCE

These person and device-specific predictions of functional capabilities of the arm allow neuroprosthesis developers to set achievable functional objectives for the systems they develop. These predictions can potentially serve as a screening tool for clinicians to use in planning neuroprosthetic interventions, greatly reducing the risk and uncertainty in such interventions.

Developing a Quasi-Static Controller for a Paralyzed Human Arm: A Simulation Study

Individuals with paralyzed limbs due to spinal cord injuries lack the ability to perform the reaching motions necessary to every day life. Functional electrical stimulation (FES) is a promising technology for restoring reaching movements to these individuals by reanimating their paralyzed muscles. We have proposed using a quasi-static model-based control strategy to achieve reaching controlled by FES. This method uses a series of static positions to connect the starting wrist position to the goal. As a first step to implementing this controller, we have completed a simulated study using a MATLAB based dynamic model of the arm in order to determine the suitable parameters for the quasi-static controller. The selected distance between static positions in the path was 6 cm, and the amount of time between switching target positions was 1.3 s. The final controller can complete reaches of over 30 cm with a median accuracy of 6.8 cm.

Holding Static Arm Configurations With Functional Electrical Stimulation: A Case Study

Functional electrical stimulation (FES) is a promising solution for restoring functional motion to individuals with paralysis, but the potential for achieving any desired full-arm reaching motion has not been realized. We present a combined feedforward-feedback controller capable of automatically calculating and applying the necessary muscle stimulations to hold the wrist of an individual with high tetraplegia in a desired static position. We used the controller to hold a complete arm configuration to maintain a series of static wrist positions. The average distance to the target wrist position, or accuracy, was 2.9 cm. The precision is defined as the radius of the 95% confidence ellipsoid for the final positions of a set of trials with the same muscle stimulations and starting position. The average precision was 3.7 cm. The control architecture used in this study to hold static positions has the potential to control arbitrary reaching motions.

Evaluating an open-loop functional electrical stimulation controller for holding the shoulder and elbow configuration of a paralyzed arm

Functional electrical stimulation (FES) is a promising solution for restoring functional motion to individuals with paralysis, but the potential for achieving full-arm reaching motions with FES for various desired tasks has not been realized. We present an open-loop controller capable of calculating and applying the necessary muscle stimulations to hold the wrist of an individual with high tetraplegia at any desired position. We used the controller to hold the wrist at a series of static positions. The controller was capable of discriminating between different wrist positions. The average distance to the target wrist position, or accuracy, was 7.7 cm. The average radius of the 95% confidence ellipsoid for a set of trials with the same muscle stimulations, or precision, was 6.7 cm. Adding feedback or online model updates will likely improve the accuracy for tasks requiring finer control. The controller is a good first step to controlling full-arm motions with FES.

Feedback control of functional electrical stimulation for arbitrary upper extremity movements

Functional electrical stimulation (FES) is a type of neuroprosthesis in which muscles are stimulated by electrical pulses in order to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. For the first time, this paper presents a feedback controller for FES to control arm movements in a 2D (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify the calculations and improve control performance. The experimental results show that this control scheme can produce arbitrary movements in the 2D task space, with less than 2 cm hand position error from the specified targets.