Dimitra Blana

Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia

Functional electrical stimulation (FES), the coordinated electrical activation of multiple muscles, has been used to restore arm and hand function in people with paralysis. User interfaces for such systems typically derive commands from mechanically unrelated parts of the body with retained volitional control, and are unnatural and unable to simultaneously command the various joints of the arm. Neural interface systems, based on spiking intracortical signals recorded from the arm area of motor cortex, have shown the ability to control computer cursors, robotic arms and individual muscles in intact non-human primates. Such neural interface systems may thus offer a more natural source of commands for restoring dexterous movements via FES. However, the ability to use decoded neural signals to control the complex mechanical dynamics of a reanimated human limb, rather than the kinematics of a computer mouse, has not been demonstrated. This study demonstrates the ability of an individual with long-standing tetraplegia to use cortical neuron recordings to command the real-time movements of a simulated dynamic arm. This virtual arm replicates the dynamics associated with arm mass and muscle contractile properties, as well as those of an FES feedback controller that converts user commands into the required muscle activation patterns. An individual with long-standing tetraplegia was thus able to control a virtual, two-joint, dynamic arm in real time using commands derived from an existing human intracortical interface technology. These results show the feasibility of combining such an intracortical interface with existing FES systems to provide a high-performance, natural system for restoring arm and hand function in individuals with extensive paralysis.

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system

A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4° in ideal conditions, and less than 10° even in the case of considerable fatigue and external disturbances.

A Real-Time, 3-D Musculoskeletal Model for Dynamic Simulation of Arm Movements

Neuroprostheses can be used to restore movement of the upper limb in individuals with high-level spinal cord injury. Development and evaluation of command and control schemes for such devices typically require real-time, ldquopatient-in-the-looprdquo experimentation. A real-time, 3-D, musculoskeletal model of the upper limb has been developed for use in a simulation environment to allow such testing to be carried out noninvasively. The model provides real-time feedback of human arm dynamics that can be displayed to the user in a virtual reality environment. The model has a 3-DOF glenohumeral joint as well as elbow flexion/extension and pronation/supination and contains 22 muscles of the shoulder and elbow divided into multiple elements. The model is able to run in real time on modest desktop hardware and demonstrates that a large-scale, 3-D model can be made to run in real time. This is a prerequisite for a real-time, whole-arm model that will form part of a dynamic arm simulator for use in the development, testing, and user training of neural prosthesis systems.

A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems

Upper extremity neuroprostheses use functional electrical stimulation (FES) to restore arm motor function to individuals with cervical level spinal cord injury. For the design and testing of these systems, a biomechanical model of the shoulder and elbow has been developed, to be used as a substitute for the human arm. It can be used to design and evaluate specific implementations of FES systems, as well as FES controllers. The model can be customized to simulate a variety of pathological conditions. For example, by adjusting the maximum force the muscles can produce, the model can be used to simulate an individual with tetraplegia and to explore the effects of FES of different muscle sets. The model comprises six bones, five joints, nine degrees of freedom, and 29 shoulder and arm muscles. It was developed using commercial, graphics-based modeling and simulation packages that are easily accessible to other researchers and can be readily interfaced to other analysis packages. It can be used for both forward-dynamic (inputs: muscle activation and external load; outputs: motions) and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations. Our model was verified by comparing the model calculated muscle activations to electromyographic signals recorded from shoulder and arm muscles of five subjects. As an example of its application to neuroprosthesis design, the model was used to demonstrate the importance of rotator cuff muscle stimulation when aiming to restore humeral elevation. It is concluded that this model is a useful tool in the development and implementation of upper extremity neuroprosthetic systems.

Musculoskeletal Model-Guided, Customizable Selection of Shoulder and Elbow Muscles for a C5 SCI Neuroprosthesis

Individuals with C5/C6 spinal cord injury (SCI) have a number of paralyzed muscles in their upper extremities that can be electrically activated in a coordinated manner to restore function. The selection of a practical subset of paralyzed muscles for stimulation depends on the specific condition of the individual, the functions targeted for restoration, and surgical considerations. This paper presents a musculoskeletal model-based approach for optimizing the muscle set used for functional electrical stimulation (FES) of the shoulder and elbow in this population. Experimentally recorded kinematics from able-bodied subjects served as inputs to a musculoskeletal model of the shoulder and elbow, which was modified to reflect the reduced muscle force capacities of an individual with C5 SCI but also the potential of using FES to activate paralyzed muscles. A large number of inverse dynamic simulations mimicking typical activities of daily living were performed that included (1) muscles with retained voluntary control and (2) many different combinations of stimulated paralyzed muscles. These results indicate that a muscle set consisting of the serratus anterior, infraspinatus and triceps would enable the greatest range of relevant movements. This set will become the initial target in a C5SCI neuroprosthesis to restore shoulder and elbow function.

Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Transhumeral amputation has a significant effect on a person’s independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person’s proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

Real-Time Simulation of Three-Dimensional Shoulder Girdle and Arm Dynamics

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

Development of a neuroprosthesis for restoring arm and hand function via functional electrical stimulation following high cervical spinal cord injury

This paper describes the development of an implanted neuroprosthesis for restoring hand and arm function to individuals with high level tetraplegia resulting from C1-C4 spinal cord injury. These individuals have complete paralysis below the level of the neck and are thus highly disabled. The neuroprosthesis under development will restore basic upper extremity movements needed for simple yet important daily activities such as eating and grooming. Simulations performed with a musculoskeletal model of the shoulder and elbow indicate that existing stimulation technology using a realistic number of stimulation channels should be sufficient for providing these functions. The neuroprosthesis will utilize 24 channels of stimulation, muscle-based electrodes for stimulation of hand muscles, and nerve cuff electrodes for stimulation of shoulder and elbow muscles. The two implanted stimulators also include a total of four implanted bipolar EMG recording channels that sample activity in neck and facial muscles. These signals, along with measurements of head orientation, will provide the user command interface for this system

Feedback control of a high level upper extremity neuroprosthesis

.......................................................................................................................... 10 CHAPTER 1: INTRODUCTION ........................................................................................ 12 Anatomy of the human upper extremity ............................................................................ 12 Spinal cord injury ................................................................................................................. 13 Functional Electrical Stimulation ........................................................................................ 14 Musculoskeletal modeling .................................................................................................... 15 Problem description .............................................................................................................. 16 Summary of research ............................................................................................................ 17 CHAPTER 2: A MUSCULOSKELETAL MODEL OF THE UPPER EXTREMITY FOR USE IN THE DEVELOPMENT OF NEUROPROSTHETIC SYSTEMS ............ 19 Abstract .................................................................................................................................. 19 Introduction ........................................................................................................................... 20 Methods .................................................................................................................................. 21 Model Construction ............................................................................................................ 21 Experimental setup.............................................................................................................. 24 Model evaluation ................................................................................................................ 27 Application to C3 SCI with and without FES ..................................................................... 27 Results .................................................................................................................................... 28 Discussion............................................................................................................................... 34 CHAPTER 3: MODEL-BASED SELECTION OF MUSCLE AND NERVE CUFF ELECTRODES FOR A HIGH TETRAPLEGIA NEUROPROSTHESIS ..................... 38 Abstract .................................................................................................................................. 38

Adaptive neural network controller for an upper extremity neuroprosthesis

The long term goal of this project is to develop an adaptive neural network controller for an upper extremity neuroprosthesis targeted for people with C5/C6 spinal cord injury (SCI). The challenge is to determine how to simultaneously stimulate different paralyzed muscles based on the EMG activity of muscles under retained voluntary control. The controller extracts the movement intention from the recorded EMG signals and generates the appropriate stimulation levels to activate the paralyzed muscles. To test the feasibility of this controller, different arm movements were recorded from able bodied subjects. Using a musculoskeletal model of the arm, inverse simulations provided muscle activation patterns corresponding to these movements. The model was modified to reflect C5/C6 SCI and the optimization criteria were varied to reflect different nervous system motor control strategies. Activation patterns were then used to train a time-delayed neural network to predict paralyzed muscle activations from voluntary muscle activations. Forward simulations were performed to obtain predicted movements and use the kinematic errors to design an adaptive strategy to account for disturbances and changes in the system.