Hello, my name is Alyssa Kubota.
I'm a 5th year PhD student in the Healthcare Robotics lab working with Dr. Laurel Riek at UC San Diego.
In this presentation, I'll be discussing our recent work in designing robot-delivered neurorehabilitation interventions
for people with mild cognitive impairment, or MCI.
In particular, I'll be describing new interaction design patterns
for how to translate cognitive interventions delivered by clinicians in clinic
to those delivered at home via robot, with a focus on how to maintain longitudinal engagement and support efficacy.
I'll also discuss design considerations for developing robots for people with MCI,
a population with unique needs and abilities which are distinct from those of older adults and people with dementia.
Covid-19 has illustrated great health disparities worldwide, particularly for minoritized populations,
who often lack access to quality healthcare services.
Robots have shown great promise for improving the accessibility of healthcare for these populations,
such as by extending longitudinal clinic-based interventions to a person's home.
The physical embodiment of robots can also promote longitudinal engagement,
enjoyment, and adherence to an intervention.
However, there are considerable barriers to developing home-deployed robots
that administer neurorehabilitation interventions to people with cognitive impairments.
Robots are just beginning to enter this space, and may increasingly be seen as an intervention themselves.
However, this is still a new concept, so there are many open questions
with regards to grounding these interventions in current clinical practice and effectively translating them to robots.
For example, how can we contextualize an intervention to the lives of users?
How should a robot behave while autonomously and longitudinally administering an intervention in a person's home?
This is what our research aims to address.
We explore this in the context of compensatory cognitive training, or CCT.
CCT teaches people metacognitive strategies to help strengthen cognitive areas
and minimize the impact of impairment on daily life.
For instance, some strategies might include using new tools such as a calendar, or trying new skills such as visual imagery.
We focus on a particular existing CCT intervention, Motivationally Enhanced CCT, or ME-CCT,
which links intervention content with a person's rehabilitation goals, within the context of MCI.
MCI is an intermediate state between normal aging and dementia,
and it impacts numerous areas of cognitive functioning including memory, attention, and executive functioning.
For the past several years, our team has worked with neuropsychologists to develop robots
that administer CCT autonomously and longitudinally to people with MCI at home.
We implemented various activities to teach and help users practice cognitive strategies, drawn from ME-CCT.
We used these prototypes as design probes
to explore how to translate clinician-delivered CCT to a robot-delivered intervention at home.
We explored the following research questions.
What are important factors for interacting with people with MCI in clinic?
How can robots longitudinally support people with MCI during CCT at home?
And in what context might a person with MCI use a robot to support CCT?
We conducted interviews with our two key stakeholders: clinical researchers and people with MCI.
We interviewed six clinical researchers in two phases.
First, we conducted individual semi-structured interviews
to explore their experiences administering CCT to people with MCI in clinic.
We didn't show our robot prototypes during this phase,
as the focus was on their general experiences working with this population.
Then, we conducted focus groups to understand how robots can longitudinally support people with MCI during CCT at home.
Each group consisted of two clinical researchers and three members of our team.
We showed video demonstrations of our robot prototypes, and storyboards of potential new activities.
We discussed roles a robot might play while delivering CCT at home
and explored how people might integrate the robot into their lives.
We also interviewed three people with MCI, all of whom previously received the same ME-CCT intervention in clinic.
With our participants with MCI, we conducted individual, semi-structured interviews
to understand the context in which they might use a robot to support CCT.
We asked about their daily lives, including their routines, challenges, and current use of technology.
Then we explored how they imagine using a robot for support in an intervention.
We showed them videos of existing social robots in the healthcare space,
and video demonstrations of our prototypes leading CCT
to help them imagine how robots could support their daily life and health.
We employed a grounded theory approach to find main themes across the interviews.
Based on our discussions with stakeholders, our data revealed eight interaction design patterns
for translating clinical interventions to robots to maintain longitudinal engagement and maximize efficacy.
These include rewarding perseverance over performance, connecting the intervention to the real world,
elating the intervention to a user's interests, goal setting, promoting engagement,
obtaining feedback from users, using reminders, and personalizing the intervention.
These patterns are intentionally broad so that they can be applied to other contexts,
and they can be combined to be more complex.
For the sake of time, I'll just discuss a few in this presentation,
but more details for all of the patterns can be found in the paper.
One of our most striking findings was the emphasis of rewarding a person's perseverance over their performance,
as they may not necessarily show improvement throughout an intervention.
When administering cognitive assessments,
clinicians may not tell people their performance to avoid influencing future performance.
A robot could reward users for behaviors such as maintaining a streak or for trying out new strategies.
Connecting the intervention to the real word is essential to improving a person's ability to transfer the content to their lives.
Clinicians might ask people to reflect on opportunities where they can practice a cognitive strategy,
such as remembering names at a social gathering.
During an intervention, a robot could similarly help people practice strategies with real world examples,
such as asking them to recall their grocery list or organizing their calendar together.
Relating an intervention to a person's interests can also make it more enjoyable.
For instance, clinicians might include family or incorporate music into an intervention to motivate people.
Robots could use similar strategies,
for example leveraging games or books that the person enjoys to make activities more interesting.
Helping users set goals for the intervention can help them be more aware of the impacts they are seeing from the intervention.
Clinicians often work closely with people to set achievable goals,
and a robot could facilitate this by asking users to reflect on their cognitive concerns and helping them identify potential solutions.
In addition to our design patterns, our discussions also revealed design considerations that are specific to people with MCI,
distinct from those of older adults and people with dementia.
Our participants discussed ways to make robots both physically and cognitively accessible to people with MCI.
For physical accessibility, clinicians were mindful that people with MCI are often older adults
and may have physical or sensory disabilities.
Thus, people should be able to adjust physical attributes such as volume or visual contrast to match their abilities.
In addition, the physical size of the robot should be small and lightweight so it's easy to move between rooms,
such as if one room is too distracting due to a TV.
However, people with MCI may often misplace personal items, so it should be large enough to not get lost easily.
The physical setup of a robot can also help people focus, such as by narrowing the area of interest.
In our case, clinicians suggested aligning the tablet with the robot to promote a focused presentation.
Furthermore, making the setup simple with few components can help reduce the risk of error.
There are also steps designers can take to make robots cognitively accessible to people with MCI,
such as by minimizing cognitive demand.
This can be achieved by keeping intervention content simple and succinct,
and organizing information in a logical manner, to help users maintain focus.
Similarly, repeating important information can help users review the material,
so it is essential to reiterate important points, and ask people if they would like anything repeated.
In addition, taking breaks can give people with MCI an opportunity to process information or clear their minds.
It also gives people an opportunity to address other points of distraction, such as answering a call or using the restroom.
Robots can also use visual aids such as icons, gestures, or facial cues to help convey important information.
At the same time, minimizing distractions from the robot and environment can help people with MCI maintain focus.
Clinicians suggested the robot's behavior could be minimal while providing information, but more engaging at other points.
We hope that these design patterns and design considerations will reduce the barriers to robot-delivered clinical interventions
in the home, and enhance the potential for robots to expand telemedical solutions.
With that, I'd like to thank my co-authors for all of their work on this project,
our labmates and collaborators for all their feedback throughout our development process, and the NSF for funding our research.
Finally, thank you for your attention, and I'll be happy to answer any questions during the full session or via email.