Using Socially Assistive Robots to Address the Caregiver Gap
From: Circuit Cellar - 07/2013 - page 80
By: David Feil-Seifer

There are looming health care and education crises on the horizon. Baby
boomers are getting older and requiring more care, which puts pressure on
caregivers. The US nursing shortage is projected to worsen. Similarly, the
rapid growth of diagnoses of developmental disorders suggests a greater need
for educators, one that the education system is struggling to meet. These
great and growing shortfalls in the number of caregivers and educators may be
addressed (in part) through the use of socially assistive robotics.

In health care, non-contact repetitive tasks make up a large part of a
caregiver’s day. Tasks such as monitoring instruments only require a check to
verify that readings are within norms. By offloading these tasks to an
automated system, a nurse or doctor could spend more time doing work that
better leverages their medical training. A robot can effectively perform
simple repetitive tasks (e.g.,  monitoring breath spirometry exercises or
post-stroke rehabilitation compliance).

I coined the term "socially assistive robotics" (SAR) to describe robots that
provide such assistance through social rather than physical interaction. My
research is the development of SAR algorithms and complete systems relevant
to domains such as poststroke rehabilitation, elder care, and therapeutic
interaction for children with autism spectrum disorders (ASD). A key
challenge for such autonomous SAR systems is the ability to sense, interpret,
and properly respond to human social behavior.

One of my research priorities is developing a socially assistive robotic
system for children with ASD. Children with ASD are characterized by social
impairments, communication difficulties, and repetitive and stereotyped
behaviors. Significant anecdotal evidence indicates that some children with
ASD respond socially to robots, which could have therapeutic ramifications.
We envision a robot that could act as a catalyst for social interaction, both
human-robot and human-human, thus aiding ASD users’ human-human
socialization. In such a scenario, the robot is not specifically generating
social behavior or participating in social interaction, but instead behaves
in a way known to provoke human-human interaction.

Enabling a robot to exhibit and understand social behavior with a child is
challenging. Children are highly individual and thus technology used for
social interaction needs to be robust to be effective. I developed an
autonomous robot that recognizes and appropriately responds to a child’s
free-form behavior in play contexts, similar to those seen in some more
traditional ASD therapies. To detect and mitigate child distress, I developed
a methodology for learning and then applying a data-driven spatiotemporal
model of social behavior based on distance-based features to automatically
differentiate between typical vs. aversive child-robot interactions. Using a
Gaussian mixture model learned over distance-based feature data, the
developed system was able to detect and interpret social behavior with
sufficient accuracy to recognize child distress. The robot can use this to
change its own behavior to encourage positive social interaction.

To encourage human-human interaction once human-robot interaction was
achieved, I developed a navigation planner that used the above spatiotemporal
model. This was used to maintain the robot’s spatial relationship with a
child to sustain interaction while also guiding the child to a particular
location in a room. This could be used ,to encourage a child to move toward
another interaction partner (e.g., a parent). The desired spatial interaction
behavior is achieved by modifying an established trajectory planner to weigh
candidate trajectories based on conformity to a trained model of the desired
behavior. I also developed a methodology for robot behavior that provides
autonomous feedback for a robot-child imitation and turn-taking game. This
was accomplished by incorporating an established therapeutic model of
feedback along with a trained model of imitation behavior. This is used as
part of an autonomous system that can play Simon Says, recognize when the
rules have been violated, and provide appropriate feedback.

A growing body of data supports the hypothesis that robots have the potential
to aid in addressing the needs of people through noncontact assistance. My
research, along with that of many others, has resulted in technical advances
for robots providing assistance to people. However, there is a long way to go
before these systems can be deployed as a therapeutic platform. Given that
the beneficiary populations are growing, and the required therapeutic needs
are increasing far more rapidly than the existing resources to address it,
SAR could provide lasting benefits to people in need.

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David Feil-Seifer, a Postdoctoral Fellow in the Computer Science Department
at Yale University, focuses his research on socially assistive robotics
(SAR), particularly the study of human-robot interaction for children with
autism spectrum disorders (ASD). His dissertation work addressed autonomous
robot behavior so that socially assistive robots can recognize and respond to
a child’s behavior in unstructured play. David received his MS and PhD in
Computer Science from the University of Southern California and a BS in
Computer Science from the University of Rochester, NY. He recently was hired
as Assistant Professor of Computer Science at the University of Nevada, Reno.

Links:
David Feil-Seifer, PhD
http://robotics.usc.edu/~dfseifer/
http://scazlab.yale.edu/people/david-feil-seifer
http://www.davidfeilseifer.com/
http://www.scazlab.com/people.php?name=david_feil-seifer

Social Robotics Lab at Yale University
http://scazlab.yale.edu/