Robin is voice assistant for people with dementia or other cognitive impairments. It won second place in the 2017 CHI Student Design Competition.
TEAM | Clair Carroll, Adena Lin, Meg Nidever, Jayanth Prathipati
ROLE | Product Research + Design
TOOLS | Sketch, InVision
METHODS | directed storytelling, competitive analysis, speed dating, usability testing, survey validation.
“Despite the numerous advances that technology has produced over the years, it often appears that large swaths of the populace are excluded or left behind.” –CHI Student Design Competition
The 2017 CHI student design competition, entitled “Leveling the playing field”, asked student designers to find a population that was not currently being served by technology or a behavior that they wished to support. Based on the research that Meg Nediver and I conducted in our Check-In project, our team decided to focus on supporting people with dementia and their caregivers.
Robin is a conversational user interface that supports independent living. By providing customizable instructions for routine tasks, quality-of-life-suggestions, and ensuring that certain daily tasks that are crucial for health are completed as normal, Robin helps users will early-stage dementia stay in their homes longer. The system consists of a mobile/web application that works with voice assistant devices such as Amazon Alexa. And although we developed Robin with dementia in mind, it could be used by anyone who would benefit from in-home routine assistance.
Dementia progresses through known stages. We identified the first four stages of dementia as the most promising for intervention, and found that in these early stages, many people are proactive and wish to learn about interventions that could help them maintain independence.
Routines are critical for living independently. Daily routines are widely used by people living independently with dementia, and caregivers do a lot to support adeherance to routines. Small deviations from routines, such as missed meals or medications, often led to negative health events, including hospitalizations.
But the exact manifestations vary a lot. While routines are hugely important for health outcomes and life satisfaction, the manifestations of these routines need to be customizable. As the disease progresses, the assertiveness of the intervention needs to increase, following what one doctor called the “reverse Piaget” of dementia.
Ease of adoption and customization are crucial. Finally, we found that the rate of abandonment of technological assistants was high among our target population, making ease of setup and customization paramount.
3 types of assistance
Directed Storytelling and interviews
We returned to the same caregivers and healthcare experts that participated in directed storytelling for the Check-In project and conducted follow-up interviews.
We reviewed assistive technologies that are currently on the market and technologies that are currently under development. We also spoke to dementia patients and their caregivers about what assistants they currently use, such as pill organizers and emergency response devices.
We read forum posts by people who had recently been diagnosed with dementia and recorded instances of needing help with certain tasks.
Drawing on our scenarios from Check-In, we identified three types of tasks for which we could provide assistance: ensuring critical tasks are completed, providing step-by-step guidance, and quality-of-life suggestions.
We then created storyboards for each of the three types of tasks and speed-dated them with caregivers and healthcare providers. From these sessions, we grew more confidant that newly-diagnosed dementia patients would be open to trying a new assistive technology.
storyboarding and speed-dating
We performed think-alouds with Robin's mobile app using both low-fi paper prototypes and hi-fi digital screens.
Finally, we returned to the dementia forums, this time asking posters if they would be interested in a voice assistant that could provide task prompting, routine guidance, and quality-of-life suggestions. 65% of respondents said that they would be interested in trying a system like Robin.
See the paper in the ACM archives here.