Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-to-target paths. We tested our trajectory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers (

Multi-Muscle FES Force Control of the Human Arm for Arbitrary Goals

We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subject’s hand was identified. We inverted the model of this redundant and coupled multiple-input multiple-output system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total root mean square error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trial-to-trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals

Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements

IntroductionIndividuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system.MethodsIn this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories.ResultsThe MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone.DiscussionTo realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities

Modeling Dynamics and Exploring Control of a Single-Wheeled Dynamically Stable Mobile Robot with Arms

This paper focuses on simulations of a dynamically stable mobile robot (Ballbot) with arms. The simulations are of Ballbot lifting its arms in various directions. A PD arm controller works independently of an LQR-designed balancing/station keeping controller. The PD controller drives the arms to follow desired trajectories. When the arms are raised, Ballbot assumes a leaning equilibrium (the physical equilibrium) as opposed to the standing equilibrium (body stands totally upright- a predefined desired equilibrium) that the LQR drives toward. The conflict between these two equilibria causes the robot to lose its balance when lifting heavy (10 kg) loads. A unified arm and station keeping/balancing controller is also described. The unified controller outperforms the independent controllers in some cases. Balancing only using arms and driving body movement with arms are briefly explored. I Acknowledgments Thanks to my advisor, Dr. Ralph Hollis for his vision in creating Ballbot and guiding my work. Thanks to Dr. George Kantor for constant technical advice and brainstorming. Thanks to Anish Mampetta for his companionship and ideas through all of our experimenting with Ballbot. Anish was also the first to derive the Ballbot motion equations using the current coordinates. Thanks to Dr. Matt Mason and Jonathan Hurst for reviewing my work as part of my committee. Thanks to my family, friends, and classmates for every da

Simple Quasi-Static Control of Functional Electrical Stimulation-Driven Reaching Motions

Functional electrical stimulation is a promising technology for restoring functional reaching motions to individuals with upper limb paralysis. We present a control architecture that combines static models of a paralyzed arm and its response to stimulation with a PID controller. The controller is used to drive the wrist of an individual with tetraplegia to a desired wrist position. We compare the performance of our controller with a feedforward component and with no feedforward component. The combined feedforward-feedback controller produced an average accuracy (defined as the distance away from the target wrist position) of 4.9 cm, and the feedback controller produced an accuracy of 4.3 cm. The combined feedforward-feedback controller produced initially larger errors than the feedback controller, but the end performance was similar. The control architecture presented has the potential to be used for arbitrary reaching motions

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

Predicting Functional Force Production Capabilities of Upper Extremity Functional Electrical Stimulation Neuroprostheses: A Proof of Concept Study

Objective. This study\u27s 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\u27s 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\u27s 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\u27s muscles. Main results. Our models classified the person\u27s 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\u27s 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