Home People Research Publications Software Contact BMES-ERC BME VSoE USC August 2008

Research BION (Gerald E. Loeb): Paralyzed muscles can be reanimated by electrical stimulation in order to prevent disuse atrophy and restore functional movement.  We have developed a new class of implantable medical devices, BION®s (for BIOnic Neurons), that can provide precise and inexpensive interfaces between electronic controllers and muscles. Separately addressable BION®s can be injected into various sites, where they receive power and digital command data from a single external RF coil. BION®s are currently being investigated in clinical trials. The following image shows the main components of the BION system: (A) the "Personal Trainer" which is programmed with digital command strategies as needed by the attending health practitioner, (B) power and digital command data coil, (C) dry tester set-up (used to confirm operational status immediately prior to implantation), (E) BION® implant®, and (F) sterilized BION® loaded in a BION® insertion tool.         Virtual prototyping of neural prosthesis (Rahman Davoodi): Movement neural prostheses for paralyzed and amputee patients are complex systems that require sophisticated control strategies to operate effectively. To facilitate the systematic design of such control systems, we have developed a sophisticated software tools known as MSMS that allow the users to develop accurate models of movement neural prosthesis for paralyzed and amputee patients. These models can be used to simulate the movement of the neural prosthesis under various control strategies and external perturbations. We have also developed a virtual reality environment where the above models can be simulated in real-time with the subject in the loop. The subject (e.g. human or monkey) can generate a variety of command signals (e.g. EMG, cortical neural activity, residual limb movement) to control the movement of the simulated limb to perform virtual rehabilitation tasks (e.g. Box and Blocks or Clothespin). The subject can see, in real-time, the stereoscopic view of the simulated limb movement from his/her own perspective via head mount displays or shutter glasses. This enables the subject to modify his/her command signals, if necessary, and learn to operate the virtual prosthesis effectively. The virtual environment enables the engineers and researchers to virtually prototype novel neural prosthetic control systems and allows the patients to evaluate and learn to operate their neural prosthesis before it is actually built or delivered to them.       Spinal control of movement (Giby Raphael): Most studies of arm and hand movements in primates have attempted to relate neural activity in motor cortex to simple features of the movement (e.g. position, force, individual muscle activity). But motor cortex does not control those features directly; it acts through the complex circuitry of the spinal cord and the mechanics of the musculoskeletal system. “The Circuitry of the Human Spinal Cord” has been well-described for simple antagonist pairs of muscles but it isn’t clear how it can be extended to multimuscle, multi-degree-of-freedom linkages. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors (spindle primary and GTO afferents) and a network of spinal circuitry based on the classical types of interneurons (propriospinal, monosynaptic Ia- excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition and fusimotor gain).  All interneurons and motoneurons had sigmoidal transfer functions that are in general agreement with cellular physiology and whose nonlinear properties lead to the types of “reflex gating” described when biological systems switch between behavioral states. The modeled system has a very large number of control inputs (~200), not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. None of the corticomotoneuronal control inputs projected directly to the spinal motoneurons in our model but the patterns of muscle coordination and movement emerged largely from the integration of descending control and afferent feedback in spinal interneurons such as the propriospinal system. We found that relatively simple and generally intuitive step-changes in a small subset of descending controls could reproduce very different but well-described behaviors.  Furthermore, details of stability and temporal patterning of muscle recruitment were surprisingly realistic, even though we made little or no attempt to optimize the values of the many other control signals and we did not modulate any of the control signals.  In fact, these behaviors appear to be robust emergent properties of the complete set of spinal circuitry as modeled.  Such properties would substantially change and generally simplify the learning of motor tasks by the brain. We then optimized the control inputs using gradient descent and hill-climbing algorithms that minimized a simple kinematics cost function. Even though the inputs were selected randomly during the initial simulation, the algorithm converged rapidly to produce physiologically realistic outputs and the solution space was found to consist of rather broad and stable local minimas. More systematic analysis is underway to determine the range of tasks and behaviors that can be modeled by this system. We are also working on a method to couple actually recorded M1 activity to our model to replicate recorded behaviors in monkeys. Partial view of the Spinal Cord Model is shown below. Click on the image to see more detailed but still partial model of the spihnal cord and the mechanical model.  Synergistic control of reaching (Rahul Kaliki): C5/C6 tetraplegic patients may be able to use voluntary shoulder motion as command signals for functional electrical stimulation (FES). We propose that natural joint synergies between the proximal and distal upper limb joints utilized during goal oriented reaching can be used as a foundation for a high level FES controller that could predict distal joint kinematics from the voluntary movements of the shoulder joint. In this study we examine the ability of artificial neural networks to fit these synergies from data recorded during reaching in 3D extrapersonal space.  Cortical control of upper extremity prosthsis (Markus Hauschild): This is a collaborative project with The Anderson Lab in Caltech in which monkeys use their cortical neural activity to control the movement of simulated prosthesis in virtual environments. Our simulation software have been used to built the virtual simulation environment to investigate the cortical control of movement. Development of tactile sensor (Nicholas Wettels): The performance of robotic and prosthetic hands in unstructured environments is severely limited by their having little or no tactile information compared to the rich tactile feedback of the human hand. We are developing a novel, robust tactile sensor array that mimics the mechanical properties and distributed touch receptors of the human fingertip. It consists of a rigid core surrounded by a weakly conductive fluid contained within an elastomeric skin. The sensor uses the deformable properties of the finger pad as part of the transduction process. Multiple electrodes are mounted on the surface of the rigid core and connected to impedance-measuring circuitry safely embedded within the core. External forces deform the fluid path around the electrodes, resulting in a distributed pattern of impedance changes containing information about those forces and the objects that applied them. Here we describe means to optimize the dynamic range of individual electrode sensors by texturing the inner surface of the silicone skin. Forces ranging from 0.1 to 30 N produced impedances ranging from 5 to 1000 kOhm. Spatial resolution (below 2 mm) and frequency response (above 50 Hz) appeared to be limited only by the viscoelastic properties of the silicone elastomeric skin. Development of vibrotactile sensor (Jeremy Fishel): Controlling grip force in a prosthetic hand requires detailed sensory feedback information about the onset of slip between the artificial fingertips and the object. In the biological hand this is accomplished with neural transducers (Pacinian corpuscles) capable of measuring micro-vibrations in the skin originating from microslips. Emulating these biological transducers is a difficult challenge for a prosthetic finger due to the fragility associated with high sensitivity transducers. By incorporating a pressure sensor into a fluid-filled fingertip we have provided a novel solution to this problem, removing the sensing elements from harm's way. Preliminary studies demonstrate that high frequency vibrations (50-800Hz) can be readily detected when such a fingertip slides across a surface. Mimicking biological grip reflexes, this provides a useful signal that would be beneficial to advanced prosthetic feedback control. Further uses of this sensor include texture discrimination and realistic tactile feedback to the user. While sliding across different surfaces, sensor output has been observed to vary in spectral content unique to the surface tested. It has been postulated that replaying these vibration signals to another part of the body could provide a crude sense of texture, further enhancing the realism of the prosthesis.