Center for Integrative Neuroscience: Using Socially Assistive Robot Assistants to Augment NeuroRehabilitation Exercise Therapy
Abstract: The Center for Integrative Neuroscience is a multidisciplinary research center that brings together neuroscientists from across campus to foster complementary and synergistic approaches to understanding the brain and neurological disorders. It supports the research and mentoring of junior faculty, as well as core facilities to provide access to modern neuroimaging techniques and to research with special neurological populations. The center is supported by an NIH/NIGMS Center of Biomedical Research Excellence (COBRE) grant (P20GM103650). COBRE grants are designed to build the faculty and resources that will strengthen an institution's research capacity and expertise in focused areas of biomedical science. Stroke and other Traumatic Brain Injuries are major causes of neurological disability. Most of those affected are left with some loss of movement, speech difficulties, and cognitive deficits. Concerted rehabilitation during the neuroplasticicty period following a stroke can help a patient recover some of their lost function. For upper-limb hemiperisis in stroke recovery, through concerted use and training of the affected limb during the critical post-stroke period, such disability can be significantly reduced. The rate and amount of recovery greatly depends on the amount of focused training, along with stroke severity and cognitive availability. Evidence shows that the intensity and frequency of focused therapy can improve functional outcomes. The goal of this project is to develop healthcare and education robots that effect positive long-term behavioral changes. This includes helping children with developmental disorders to socialize in a positive way, encouraging positive user health choices, and assisting in physical rehabilitation. Since such rehabilitation normally requires supervision of trained professionals, lack of resources (i.e., workforce shortage, insurance shortfalls, patient non-compliance) limits the amount of time available for supervised rehabilitation. As a result, the quality of life of patients with TBI or stroke is dramatically reduced, and medical costs and lost productivity continue to be incurred. In addition, a growing rate of diagnosis, an aging population, and geographic disparities are contributing to inadequate health resources to meet the care needs. Socially Assistive Robotics (SAR) can potentially address these care caps. A critical deficit for the development and adoption of SAR in care scenarios is the lack of performance and study of long-term Human-Robot Interaction in care settings. While there has been an explosion of research into HRI over the last decade, a large majority of this work examines short-term SAR scenarios. Furthermore, studies that have examined long-term HRI scenarios have been very specialized in nature, none of which involves neurorebailitation care for patients with TBI. This project aims to bridge that gap.
- Organization: National Institutes of Health (NIH)
- Award #: P20GM103650
- Amount: $150,000
- Date: Sept. 1, 2017 - May 31, 2018
- PI: Michael Webster
Dr. David Feil-Seifer
- Miller, B., Anderson, M., Major, J., Jurkiewicz, M., Kirn, A., & Feil-Seifer, D. Unplugged Robotics to Increase K-12 Students' Engineering Interest and Attitudes. In Frontiers in Education, San Jose, CA, Oct 2018. ( details ) ( .pdf )
Perceptions of Social Intelligence Scale Sept. 1, 2017 - Present
Social intelligence is the ability to interact effectively with others in order to accomplish your goals (Ford & Tisak, 1983). Social intelligence is critically important for social robots, which are designed to interact and communicate with humans (Dautenhahn, 2007). Social robots might have goals such as building relationships with people, teaching people, learning something from people, helping people accomplish tasks, and completing tasks that directly involve people’s bodies (e.g., lifting people, washing people) or minds (e.g., retrieving phone numbers for people, scheduling appointments for people). In addition, social robots may try to avoid interfering with tasks that are being done by people. For example, they may try to be unobtrusive and not interrupt. Social intelligence is also important for robots engaged in non-social tasks if they will be around people when they are doing their work. Like social robots, such task-focused robots may be designed to avoid interfering with the work of people around them. This is important not just for the people the robots work with, but also for the robots themselves. For example, if a robotic vacuum bumps into people or scares household pets, the owners may turn it off. In addition, task-focused robots will be better able to accomplish their goals if they can inspire people to assist them when needed. For example, if a delivery robot is trying to take a meal to a certain room in a hospital and its path is blocked by a cart, it may be beneficial if it can inspire nearby humans to move the cart. While previous research on human-robot interaction (HRI) has referenced and contained aspects of the social intelligence of robots (Bartneck, Kulic, Croft, & Zoghbi, 2009; Ho, MacDorman, 2010; Ho, MacDorman, 2017; Moshkina, 2012; Nomura, Suzuki, Kanda, & Kato, 2006), the concept of robotic social intelligence has not been clearly defined. Measures of similar concepts are brief and incomplete, and often include extraneous variables. Moreover, measures of human social intelligence (e.g., Baron-Cohen, S. Wheelwright ,& Hill, 2001; Silvera, Martinussen, & Dahl, 2001) cannot be adapted for robots, because they assess skills that current and near-future robots do not have and because they omit basic skills that are essential for smooth social interactions. Therefore, we designed a set of 20 scales to measure the perceived social intelligence of robots.