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Robotics Lab: Research

Development of VRROOM (Virtual Reality Robotics and Optical Operations Machine)

James Patton, Robert Kenyon, Sandro Mussa-Ivaldi, Alon Fishbach, Felix Huang, Assaf Dvorkin, Mark Kovic, Sarah Housman, Ross Bogey

A key development for the lab is a new system that uses augmented reality, motion analysis, and robotics technology. In order to achieve significant practical application in rehabilitation, human-interface robots must safely operate in three dimensions with a large workspace and an appropriately designed visual interface. We are developing instrumentation that allows large movements with specialized forces from the robot and visual feedback. The display superimposes images on the real-world, allowing practice of everyday tasks. A state-of-art augmented reality (AR) display system is combined with a robot and motion sensors. This technology allows possibilities beyond real world experience, such as having the computer single out and magnify the subject's movement errors. One challenge is that the patient's desired movement is needed to determine the error, which is not always available. Our new thrusts are to develop practical clinical approaches for determining and exploiting the subject's desired movement or therapist instruction in order to calculate the error.

Delays and Time

Assaf Pressman, Sandro Mussa-Ivaldi

Advanced technology has recently provided truly immersive virtual environments with teleoperated robotic devices. In order to control movements from a distance, the human sensorimotor system has to overcome the effects of delay. Currently, little is known about the mechanisms that underlie haptic estimation in delayed environments. The objective of this study is to determine the effect of sensory and environmental delays on perception of surfaces stiffness. The results of the experiment indicate a systematic dependence of the estimated stiffness upon the delay between position and force.

Error Augmentation

James Patton, Robert Kenyon, Sandro Mussa-Ivaldi, Mark Kovic, Sarah Houseman, Ross Bogey

A two degree-of-freedom robotic system was used to investigate how different methods of performance error feedback can lead to faster and more complete motor learning in individuals asked to compensate for a novel visuo-motor transformation (a 30 degree rotation). Our results demonstrate that error-augmentation can improve the rate and extent of motor learning of visuomotor rotations in healthy subjects. We also tested this method on straightening the movements of stroke subjects, and our early results suggest that error amplification can facilitate neurorehabilitation strategies in brain injuries such as stroke.


Sarah J. Housman, Vu Le, and David J. Reinkensmeyer

The goal of this project is to test a device which allows stroke survivors to practice arm movement therapy with indirect therapist supervision. The Therapy-Wilmington Robotic Exoskeleton (T-WREX) was developed at the University of California-Irvine. This device was designed for adults with significant arm weakness resulting from stroke, and provides intense movement training without continuous supervision from a therapist. T-WREX is a five degree-of-freedom passive antigravity orthosis and computer workstation. This project is currently investigating two different approaches for upper extremity training with adult stroke survivors. One intervention involves training with the T-WREX and the other involves traditional arm exercises.

Machine Learning

Zach Danziger

Patients who no longer have the use of their limbs now have access to various technologies which can harness the volitional signals that their bodies can still produce, such as EEG electrode hats, eye movement recognition, or position sensors worn over a garment. Each one of these technologies are cumbersome to learn and awkward to control, making everyday locomotion extremely difficult. With the aid of an effective computer learning algorithm learning these control paradigms will become easy, and control can become graceful, greatly improving quality of life. The goal of this study is to create and examine a machine learning algorithm that adapts in a controlled and cadenced way to foster a harmonious learning environment between the user and the machine.

Robotic Orthosis for Gait in Stroke

James Sulzer

It is commonly held that the cause of stiff-knee gait in stroke is due to inappropriately timed or graded muscle activity in the knee extensors and flexors. The object of this study is to develop a lightweight orthosis that can assist the knee at key points in the gait cycle, but feel transparent when necessary. This orthosis, called SERKA (Series Elastic Remote Knee Actuator) uses remote actuation and series compliance to create a safe, lightweight and transparent knee actuator. The results of this work will lead to a better understanding of the mechanisms of stiff-knee gait, and perhaps result in the design of a portable gait orthosis.

Proprioceptive Feedback

Mark Shapiro, Cynthia Poon, Fabian David, Minos Niu, Daniel Corcos

Proprioceptive input from muscle spindles can be used by the CNS for feedback control during movement. The gains of the spinal and supra-spinal feedback loops are centrally modulated depending on the desired movement speed, expected load, and other parameters of the task.

Abnormalities in the descending control of the proprioceptive feedback pathways are believed to underlie motor impairments in spasticity and Parkinsonian rigidity. The objective of this research is to investigate the descending control of proprioceptive feedback during movement.

For this study, a subject makes a series of arm movements to a target. The robot unexpectedly changes the movement trajectory, and the responses in muscle electromyogram are analyzed.

VR-based assessment tool for spatial neglect

Assaf Dvorkin, James Patton

The neglect syndrome, which is characterized by a failure to respond to stimuli that appear on the side of space opposite the lesion, is a complex disorder of spatial representation and attention. The current methods of assessing neglect are poor and insensitive. Most of them take place on 2 dimensional surfaces which do not reflect the reality of a 3 dimensional world. We are exploring novel possibilities of robotics and virtual reality technology for assessment of neglect in multiple spatial dimensions. Our initial results support the hypothesis that neglect patients exhibit spatial bias in more than one spatial dimension simultaneously.

Controlling Assistive Devices

Alon Fishbach

Many patients, especially tetraplegic, have difficulties in maneuvering and steering their wheelchairs. The combination of a patient's limited mobility and an interface that requires a precise and inflexible manipulation is a source of problems for these patients. This highlights the need for the development of control interfaces that are tailored to the specific residual motor skills of the patient. Motion tracking technology offers a convenient and flexible way of capturing the motions of a patient with limited mobility, as sensors can be placed virtually anywhere on the body and measure very small motions. We are investigating and developing new methods for controlling assistive devices (e.g. powered wheelchairs) using wearable sensors. Controlling the external device (wheelchair) is practiced in a virtual reality environment, which allows the device's interface to gradually change so as to fit with the patient's evolving skills. At the end of this process, we plan to apply the evolved interface to the actual device (wheelchair or robot) and test the efficacy of learning in the real-life context.

Training with Enhanced Interactive Priming

FC Huang, FA Mussa-Ivaldi, JL Patton

Allowing free manual exploration may allow fuller representation of how to compensate for mechanical and neuro-muscular constraints. However, naive exploration of an unfamiliar environment may be ineffective, since it is not known a priori what actions lead to the most revelatory information. In a preliminary study for neurologically intact subjects, we found that free manual exploration of an inertial load augmented that was augmented with negative damping led to better movement planning for conditions. In our approach, designing the mechanics of the task could facilitate motor plan formation by prompting more effective exploration. We are currently developing a study involving stroke survivors to test the efficacy of training with robot applied loading that exaggerates the inertial dynamics of the affected arm.

Brain Machine Interface

Alessandro Vato, Marianna Semprini

Nowadays restoring lost sensory and motor functions for immobilized patients is one of the major challenges for scientists, clinicians and engineers. Many researchers of neural engineering have demonstrated interest in studying and developing brain-machine interfaces (BMIs) with the aim of building motor neural prostheses for patients suffering from a variety of sensory-motor disabilities. Current motor BMIs are operated by patients based on visual information. However, the visual system has an intrinsic delay ranging between 100 and 200 ms. This is inadequate for on-line corrections of natural movement, particularly if mechanical contacts with the environment are involved. Artificial proprioception could significantly reduce the delay of usable feedback. The main goal of this project is to produce artificial proprioception by stimulating directly the somatosensory cortex of rats while performing complex motor tasks.

KineAssist (TM)

Jamie Burgess, James Patton, David Brown

It has recently been shown that loading of the paretic limb due to gravity imparts a negative effect on elbow extension in the horizontal plane due to abnormal descending commands (Beer et al, 2007). Additionally for patients with stroke, current research suggests that an inappropriate integration of peripheral afferent input, such as from load-related signals, leads to abnormal descending control of gait (Dietz 2002). Studying how sensory information related to load is processed in the stroke-injured nervous system will help improve current interventions utilizing body weight support. The objective of this project is to explore the integration of feedback and feedforward control of locomotion is altered due to varying levels of load presented through suspension and locomotor effort in both the healthy and stroke impaired nervous system.


James Sulzer

Chronic stroke survivors lack sufficient outpatient therapy, despite indications that more therapy at the chronic stage can restore some function. Both insurance and physical constraints on therapists prevent training in the home, most likely where this activity would take place. Nevertheless, this gap reveals a promising application for robots, low-cost home care.

A robot designed for home use needs to be inexpensive, portable and safe. Earlier, we have explored a type of compliant variable transmission known as the MARIONET (Moment arm Adjustment for Remote Induction Of Net Effective Torque). The proof-of-concept, behaving similar to a rotary Series Elastic Actuator, has been found suitable for low-cost, light weight applications. This paper discusses further analysis of the singlejoint MARIONET and proposes the design for the new planar, upper extremity two-joint manipulandum for clinical and home use.

Enhancing Learning in Laparoscopy

FC Huang, FA Mussa-Ivaldi, JL Patton

We are interested in investigating how individuals learn how to perform skillful manipulation of a laparoscopic tool. Medical students report particular difficulty in learning complex maneuvers in laparoscopic surgery, especially those involved in intra-corporeal knot-tying. We speculate that the difficulty involved can actually be divided in several component skills, including learning the kinematic transformation due to tool pivoting, interpreting visual cues in the context of a spatial mapping, and movement planning that integrates both tool tip position and hand posture. Furthermore, we hypothesize that training can be accelerated by presenting feedback conditions that enhance the learner's perception of the appropriate sensory-motor transformations. In a human subject study involving medical students, we intend to investigate learning and generalization of manipulation skills in a laparoscopic tool simulation that present visual feedback while tracking hand position and posture. We will analyze hand and tool movement trajectories and characterize learning in terms of systematic distortion to path, final targeting error, variability.

Reorganizing Muscle Synergies

D Saha, FA Mussa-Ivaldi

For most isometric force contractions several muscle activation patterns can be used to perform the task. Yet, studies have shown that subjects tend to exhibit the same activation pattern for a given task. This study examines whether muscle activation patterns associated with an isometric force contraction task can be changed through training in able-bodied individuals. Studying the plasticity of muscle activation patterns in healthy individuals may help us design rehabilitation protocols aimed at overcoming abnormal muscle synergies exhibited by stroke patients.

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