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Jim Patton


Biomedical Engineering Faculty

Dr. Jim Patton, Ph.D.


James L. Patton, Ph.D., Professor of Biomedical Engineering, has a broad background in control of movement and neurorehabilitation, with over 20 years of experience in the field of robotics, gait, balance, motor control, and rehabilitation engineering. He has been PI or co investigator on several previous NIDRR, American Heart, NSF, and NIH-funded grants, and has successfully mentored a variety of research projects leading to peer-reviewed publications related to human machine interactions, adaptive human motor control, learning, and rehabilitation. This research builds on his ongoing body of work on novel, and often exotic, programming of robotics for use and development of rehabilitation frameworks. Dr. Patton is also the director of the NIDRR Center for Rehabilitation Robotics, which has supported over 10 major research projects and numerous initiatives that further the cause of using technology for training and restoring function.


In Dr. Patton’s lab, a high school teacher can choose from a variety of projects:

1) Stroke subject data collection on the isometric project
The isometric experiment asks one fundamental question in motor learning: Can an individual learn to move better while not moving? Forces they exert move the limb of a virtual avatar. We have already shown that healthy people perform better doing the real task after they have practiced without moving. This has ramifications in any training that might be dangerous or overly difficult, but also may be useful in any population that has unwanted spasms and other pathology as they move. The goal now is test this idea on patients who have trouble moving after a stroke. In a hand‐held robotic reaching task, we will test the very simple hypothesis that patients that train for 40 minutes using this isometric training will end up performing better later when tested doing the real motion. All protocols have been established and patients have already been collected. Here we are simply gaining more data and seeing whether the group performs better.

2) Design, construction, and validation of a planar, sensed, unactuated manipulandum
Experiments using physical deficit surfaces are just one example of why a simple system might be useful. Back to the experiments of Morasso many years ago through the visual distortion experiments, a lot of simple experiments do not need motors. This can be prototyped as an “inexpensive as possible” design. Perhaps this could be made out of toy and inexpensive parts. This is less research and more design for simplicity. One goal will be to develop design specifications, and then validate based on these.

3) Traveling salesman task
The “traveling salesman” is an important problem in computer science: visit a bunch of locations in space while minimizing the total time (or distance or effort). This task is intermediate between fully free exploration and reaching repeatedly to the same for targets. Because the subject can visit them in whatever order they choose, we gain important extra information about choice as well as the classical information about targeted reaching. The benefits are: 1) This helps to encourage subjects to explore the full workspace, 2) it is a task that is not random so one can observes discrete actions without repeats, and 3) one can explore the quality of good longer‐term planning. We would pilot this study on healthy, quantify the results of the solution, and then perhaps compare patients’ abilities in this task.

4) Error Fields applied to stroke survivors
A new form of error augmentation has been evaluated on healthy subjects to enhance the learning process, where one’s errors are artificially enhanced using each subject’s own statistical error tendencies. We have begun testing this on stroke patients where motor deficits are very unique to each individual. All protocols have been established and patients have already been collected. Here we are simply gaining more data and seeing whether the group performs better.

5) Free exploration with electromyography (EMG) and distribution analysis
Considering humans operate in a 2nd order world, meaning we create forces and torques in order to move, it would make sense to then characterize individuals’ movements by the muscles that produce forces. We believe that analyzing statistical distributions of self‐directed motor exploration data offers a detailed view of how people tend to move. We have thus far limited our analysis to the state space variables (position, velocity, acceleration) that result from output forces. Electromyography (EMG) is an technique for evaluating and recording the electrical activity produced by skeletal muscles. We want to identify how distributions of EMG are affected by stroke. This project includes collecting and analyzing EMG data while subjects perform a motor exploration task (using a computer mouse). We hypothesize that visual feedback devised to encourage EMG to unvisited regions will result in new tendencies after training. This could be made even easier with just forces. Isometric forces. Spanning the space with forces.

6) 3D free exploration & histograms data
To better understand movement deficits in 3D reaching, we are trying to identify how the distributions of movement in a number of are affected after individuals have sustained a stroke. We hypothesize that specialized distorting visual feedback devised to encourage movement to unvisited regions will result in new tendencies after training.

7) Looking Glass development for therapists
Our successful clinical work identified the need for an interface for the robots we use. This involves building and testing several aspects of the virtual reality and robotics system we will use in the lab of the future. This includes a new forearm with human interface attachment for the proficio robot, a new set of software for the proficio and visual display.This needs to have a universal or ball‐and‐socket joint, along with the ability for subject to rotate their wrist. We would also like to incorporate this with exotendon hand‐opening device. This may involve computer‐aided design and 3‐D printing of some needed orthosis parts.

8) Gravity assist with rods, tubes, or rolls
Exoskeletal linkages that use nonlinear springs so that they might match and cancel out gravity are a common approach to make someone’s arm feel weightless. Continuation of this project to help develop a full arm mechanism from rolled paper. The goal would be to design and prototype a device, first as a scaled mock‐up and demonstrate (approximate) gravity compensation. One could explore this with rolled paper vs plastic tubes vs fiberglass rods.

9) Expanding the MARIONET design
Simple exoskeletons for assistance, therapy, and motor control studies. These are human‐friendly mechanisms that are versatile, cheap, and lightweight. Many of the classic robotic devices can be designed to be simple, non‐intimidating, and inexpensive using spring‐loaded designs.

10) Device that detects trunk motion
Stroke survivors’ reaching actions often include compensatory actions, such as trunk motion, in order to facilitate motion and achieve desired positions. In the past, we have used shoulder straps to limit undesirable trunk motion presumably for more controlled science, but this type of restraint is uncomfortable, inhibiting, and unnatural. Here we aim to design a device that instead just measures trunk motions, and can provide feedback to the patient to actively reduce these compensatory movements. We will test the hypothesis that this type of device directly reduces the compensatory actions.