Demo Data from SIGIR 2026 / reviewers in the wild / expert
Rainee Pei
dblp:341/9530
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-5149-8537ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 56% Health and well-being technologies · 44% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › assistive robotics
cognitively assistive robots |
0.7 | 1 | 2023 | Get SMART: Collaborative Goal Setting with Cognitively Assistive Robots · HRI 2023 |
Health and well-being technologies › rehabilitation
neurorehabilitation |
0.7 | 1 | 2023 | Get SMART: Collaborative Goal Setting with Cognitively Assistive Robots · HRI 2023 |
Health and well-being technologies
goal setting |
0.2 | 1 | 2023 | Get SMART: Collaborative Goal Setting with Cognitively Assistive Robots · HRI 2023 |
Health and well-being technologies
mild cognitive impairment |
0.2 | 1 | 2023 | Get SMART: Collaborative Goal Setting with Cognitively Assistive Robots · HRI 2023 |
Methods — techniques the papers use, named apart from their topics
prototyping · 0.7co-design · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Get SMART: Collaborative Goal Setting with Cognitively Assistive RobotsabstractMany robot-delivered health interventions aim to support people longitudinally at home to complement or replace in-clinic treatments. However, there is little guidance on how robots can support collaborative goal setting (CGS). CGS is the process in which a person works with a clinician to set and modify their goals for care; it can improve treatment adherence and efficacy. However, for home-deployed robots, clinicians will have limited availability to help set and modify goals over time, which necessitates that robots support CGS on their own. In this work, we explore how robots can facilitate CGS in the context of our robot CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), which delivers neurorehabilitation to people with mild cognitive impairment (PwMCI). We co-designed robot behaviors for supporting CGS with clinical neuropsychologists and PwMCI, and prototyped them on CARMEN. We present feedback on how PwMCI envision these behaviors supporting goal progress and motivation during an intervention. We report insights on how to support this process with home-deployed robots and propose a framework to support HRI researchers interested in exploring this both in the context of cognitively assistive robots and beyond. This work supports designing and implementing CGS on robots, which will ultimately extend the efficacy of robot-delivered health interventions. Alyssa Kubota, Rainee Pei, Ethan Sun, Dagoberto Cruz-Sandoval, Soyon Kim, Laurel D. Riek |
HRI | 2 |