Yoky Matsuoka - US grants

DESCRIPTION (provided by applicant) In this proposed research, we intend to develop two new techniques which will be used to identify a highly effective robotic rehabilitation strategy. Animal models will be used to address issues that cannot be addressed using human patients. Currently, several robotic stroke rehabilitation techniques are being evaluated to determine their effect on human patients' short and long term recovery performance. Robots, for example, are being attached to patients' limbs and applying force to move them as physical therapists routinely do. Such robotic techniques, however, are simple extensions of what physical therapists are already doing, and the only outcome measurements available are patients' behavioral changes. Robots are currently being used on a limited basis in stroke rehabilitation research because it is not ethical to test a variety of robot force fields or techniques on humans if these fields or techniques have not been proven to have a positive effect on them. Therefore, we believe that evaluating robotic rehabilitation techniques on animal models is crucial. To our knowledge, animal models have never been used to evaluate robotic rehabilitation of stroke. To use animal models, we must develop two new techniques that have not yet been explored. First, we will develop a technique to produce a precise lesion in an animal that simulates a stroke without risking the animal's survival rate. To do this, we will use a non-invasive photochemical technique. Second, we will design, construct, and test a new robot controller technology for animals. We will rehabilitate animals using this new robotic controller which will later be applicable to human rehabilitation techniques. We will combine these techniques to establish the superiority of robot-assisted intervention over non-assisted rehabilitation, explore the optimal training schedules, and identify gene products that are selectively modulated following robotic rehabilitation. The results generated in this project will be used as preliminary results to apply for an R01 grant in which effective robotic force assistance will be investigated to identify the optimal therapeutic solution for robotic rehabilitation. We have no doubt that the experimental results we produce with these techniques will significantly affect the field of rehabilitation.

This project, investigating human grasping and manipulation strategies to gain insight into synergies employed by human subjects in grasping and manipulation tasks, aims to explore how best to use these findings to create better control algorithms for robot hands. Of particular importance are techniques to make autonomous robot behavior robust to uncertainties and to make algorithms with a human in the loop, such as teleoperation and control of prosthetic devices, more intuitive and effective for fine manipulation tasks. Although a high degree of freedom (DOF) device may be required to manipulate a wide variety of objects and perform a wide variety of tasks, in any given task situation, only a small number of independently controlled DOF may be necessary. To make significant progress towards dexterous grasping and manipulation, we must:
Make the best possible use of available sensing technology (specially for force sensing), and
Understand how to analyze, plan, and control hand motion in a reduced degree of freedom space (take advantage of task-based coordination rules and synergies that may make real time grasp optimization and planning tractable).
The infrastructure should contribute in answering the following questions:
What is an optimal grasp, and how does it depend on the kinematic and dynamic properties of the device doing the grasping?
What is the relationship between critical signals, muscle activation levels, hand shape, and force production during task performance?
Does analysis of data collected along the pipeline from cortical signals to force production allow easy organization into grasp primitives or result in "control handles" that a human operator of a robot hand would find intuitive?
How can human demonstrations of grasping and manipulation tasks be employed as the wonderful resource they seem to be, i.e., how can individual examples be converted into control algorithms that will function on a robot and be robust to variations and uncertainties?

Broader Impact: The results will improve the understanding of human hand motion and force production, the use of force information in teleoperation and control of prosthetic devices, and the ability to coach robot behavior through task demonstration. Teams of undergraduate students will use the facility and data collected will be made available on the web.

[unreadable] DESCRIPTION (provided by applicant): Due to advances in modern medicine, the elderly population is growing worldwide, and, along with this growth, there is a growing need for physical rehabilitation after strokes, which have an average onset of over 65 years of age. Given the magnitude of this problem and its societal ramifications, the time is ripe to explore the extent to which robotic devices and virtual environments can be used as a means of rehabilitation to improve the quality of life for both the elderly and the physically disabled. According to recent studies in robotics, robot-assisted stroke rehabilitation enhances arm movement recovery. Moreover, robot-assisted rehabilitation improves patients' mobility and strength to the point where it is equal to, or greater than, that which is achieved by human-assisted therapy. However, none of the currently available systems addresses patients' perceptual or cognitive deficits. Furthermore, these systems neglect to address the fact that many patients do not reach their full mobility potential using these systems. To remedy these problems, a virtual robotic environment that explores the full potential needs and abilities of patients must be developed. We will coin this strategy, "rehabilitation by distortion." To develop an environment to rehabilitate by distortion, however, there are two fundamental issues to address. First, it is necessary to quantify the perceptual gap that can be created between virtual and actual movements that is not perceptible to patients. To do so, we will first quantify the lowest sensory resolution, also known as just noticeable difference (JND), of force and position. As we explain below, the JND will act as the lowest bound of the perceptual gap. In addition, we will quantify the size of the perceptual gap with the existence of visual feedback distortion. Second, having identified a perceptual gap that is not noticeable, we must prove that mobility and strength of stroke patients can be extended by undetected distortion. For this proposed work, we will isolate working with one finger. Fingers are one of the parts of the body that are most commonly affected by strokes; thus testing the basic concepts about the perceptual gap between virtual and actual movements using fingers is appropriate. After we prove that rehabilitation by distortion is therapeutic for a finger, we can expand our work to other limbs. In addition, to show that this strategy is effective for patients who already received traditional therapy, we will work with patients who have already completed their traditional therapy and are at least one year after the onset of strokes. If we prove that we can allow patients to move beyond what they thought was possible after traditional therapeutic techniques, the results will be groundbreaking and will lead to an R01 grant to develop a new robotic virtual therapeutic strategy, "rehabilitation by distortion," that extends the force production and range of motion for motor impaired patients recovering from stroke. [unreadable] [unreadable]

With the growth of the elderly population, finding a way to enhance quality of life and safety with minimum human assistance will become critical in the next few decades. Japanese robotics researchers have been focusing on research and applications in these areas in light of the aging Japanese population. U.S. robotics researchers have also been working on the same areas but from different viewpoints. Even though both Japan and the U.S. are conducting research on the same areas, there has been virtually no collaboration between U.S. and Japanese researchers in these research areas due to the lack of appropriate funding structures and mechanisms.

The goal of this workshop is to initiate and facilitate collaborations between Japanese and U.S. researchers in the areas of robotics for safety, security and society. The proposed workshop will bring 16 Japanese researchers and 16 U.S. researchers together to discuss their research, and to explore mechanisms for joint collaborations to advance research in and improve Safety, Security, and Society. As the collaborative research evolves, enhanced research results will be published as collaborative work, applications will affect society, and ideas will be generated for future collaborative structures.

0930908
Rao


Brain-machine interfaces (BMIs) are devices that allow a subject to control objects directly using brain signals. Such devices offer the potential to significantly improve the quality of life of locked-in, paralyzed, or disabled individuals by allowing them to communicate via virtual keyboards and control prosthetic robotic devices. The two dominant paradigms for brain-machine interfacing today rely on non-invasive recording from the scalp (EEG) and invasive techniques based on intracortical implants. EEG signals are extremely noisy, thereby limiting the bandwidth of control signals that can be reliably extracted. Intracortical implants on the other hand yield stronger signals but pose serious health risks.

In this proposal, the PI describes a research program for investigating BMIs based on electrocorticography (ECoG), a relatively new technique that involves recording signals subdurally from the brain surface. These signals have much higher signal-to-noise ratio than EEG signal while at the same time, pose lesser risks than techniques that penetrate the brain surface. The proposed research will address the following key issues:

(1) Exploiting high frequency ECoG signals for BMI: Recent work has shown the existence of broad-spectral ECoG changes at high frequencies during movement and imagery. The PI and his team will explore the application of such ECoG modulation for multi-dimensional control in BMIs.
(2) Neural plasticity of local cortical circuits during BMI: The PI's team will investigate the dynamic range of the spectral changes in ECoG and analyze the adaptations that occur due to brain plasticity during BMI control. This will help pave the way for controlling 3 or more degrees of freedom in a BMI from a single control electrode.

(3) Abstraction of control signals: After extended periods of BMI use, many patients report no longer imagining moving a control limb but rather concentrating on the desired result of the BMI task itself. The PI and his team will explore the creation of new cortical communication pathways underlying such abstraction and leverage these new control signals in expanding the bandwidth of the BMI.
(4) Applications of new control signals to novel BMI paradigms: The BMI techniques will be tested using virtual devices such as cursor-driven menu systems for communication as well as more complex robotic systems such as a prosthetic robotic hand and a humanoid robot.
The educational component of the project involves curriculum development, interdisciplinary training for graduate and undergraduate students, and outreach to K-12 students.

Intellectual Merit: The proposed research represents one of the first efforts to exploit ECoG and the brain's plasticity to build BMIs that can control devices with large degrees of freedom. The study of abstraction of control signals and its application to robotic BMIs is also novel.
Broader Impact: If successful, this research will lead to new ECoG-based BMI systems that will surpass the abilities of current BMIs by relying on the brain's ability to adapt to novel control scenarios and leveraging the large-scale population-level electrical activity measured by ECoG. The project will enable the training of graduate students in a multidisciplinary environment. Promising undergraduates, including students from underrepresented groups, will gain valuable research experience in preparation for industrial and academic careers. A K-12 outreach effort will enable students from local area schools to visit the laboratories of the PIs and gain hands-on experience in the emerging field of brain-machine interfaces.

Over the last decade, the field of neural engineering has demonstrated to the world that a computer cursor, a wheelchair, or a simple prosthetic limb can be controlled using direct brain-machine and brain-computer neural signals. However, technologies that allow such accomplishments do not yet enable versatile and highly complex interactions with sophisticated environments. Today's intelligent systems and robots can neither sense nor move like biological systems, and devices implanted in or interfaced with neural systems cannot process neural data robustly, safely, and in a functionally meaningful way. Doing so requires a critical missing ingredient: a novel, neural-inspired approach based on a deep understanding of how biological systems acquire and process information. This is the focus of this proposal.

The NSF ERC for Sensorimotor Neural Engineering (ERC/SNE or "Center") will become a global hub for delivering neural-inspired sensorimotor devices. Using devices that mine the rich data in neural signals available from implantable, wearable, and interactive interfaces, the ERC/SNE will build end-to-end integrated systems. Examples include: implantable neurochips that can activate paralyzed limbs by electrically stimulating muscles or nerve roots; stationary robots that extract neural signals from a user's touch to provide home-based, post-stroke therapy; neural-controlled adaptive prosthetic limbs that provide sophisticated sensory feedback, and wearable caps that control external exploration devices. Unlike traditional approaches that stress accommodation to the needs of people with neurological disabilities, the ERC/SNE will focus on proactive technologies that provide seamless and adaptive person-machine interaction. It will accomplish this mission with three core engineering thrusts: (1) communication and interface design for devices and data management, (2) reverse and forward engineering of neural systems and neural-inspired devices, and (3) control and adaptation technologies that express sensorimotor functions for individual needs.

The ERC/SNE will nurture future global multidisciplinary leaders. It will develop middle and high school project-based curricula that introduce neural engineering principles to students underrepresented in engineering. It will create multi-institution, undergraduate and graduate Neural Engineering courses with new degree structures and develop vertical research mentoring chains to build a strong research culture from faculty to K-12. It will build long-lasting and deep relationships through faculty and student exchange programs across all disciplines and partnering institutions, with a goal of removing barriers in communication across different fields, countries, and diverse backgrounds. The neural engineering field creates new pathways from the less quantitatively-based biological sciences to the more quantitatively-based engineering fields as well as pathways for people with disabilities to work in an engineering field that addresses their own experience and needs. The women and underrepresented minorities who currently account for over 40% of the Center's leadership team will serve as role models for students and starting faculty. Further, the ERC/SNE will extend its impact by identifying key technologies according to market significance and technical risk. The Center's portfolio will be constructed to deliver a steady stream of innovations over the near and long term. Its industry partnership structure includes not only small and large firms that will help shape Center IPs, but also hospitals and investment firms that will ground research activities to technologies that will truly assist people in need and steer future neural engineering market directions.

The ERC/NSE will strive to enhance the human experience both for persons with neurological disabilities and for the coming generation of global and diverse engineering innovators. The Center's seasoned, multi-disciplinary team will transform healthcare, manufacturing, and the educational infrastructure to guarantee neural engineering global leadership.