Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jeannette Elliott
dblp:281/1658 · also Jeannette R. Elliott
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0002-4355-3444ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Applied, interdisciplinary, general and emerging 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 |
Accessibility and assistive technology · 67% Interaction techniques and input · 33% | |
| Artificial intelligence
1 paper |
Motion planning and robot control · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › hands-free interaction
hands-free input |
0.8 | 1 | 2024 | Driving a Ballbot Wheelchair with Hands-Free Torso Control · HRI 2024 |
Accessibility and assistive technology
mobility assistance |
0.8 | 1 | 2024 | Driving a Ballbot Wheelchair with Hands-Free Torso Control · HRI 2024 |
Accessibility and assistive technology › mobility assistance
wheelchair control |
0.8 | 1 | 2024 | Driving a Ballbot Wheelchair with Hands-Free Torso Control · HRI 2024 |
Robotics › Motion planning and robot control
omnidirectional mobile robot |
0.2 | 1 | 2024 | Driving a Ballbot Wheelchair with Hands-Free Torso Control · HRI 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 1.5repeated-measures MANOVA · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Driving a Ballbot Wheelchair with Hands-Free Torso ControlabstractA novel wheelchair called PURE (Personalized Unique Rolling Experience) that uses hands-free (HF) torso lean-to-steer control has been developed for manual wheelchair users (mWCUs). PURE addresses limitations of current wheelchairs, such as the inability to use both hands for life experiences instead of propulsion. PURE uses a ball-based robot drivetrain to offer a compact, self-balancing, omnidirectional mobile device. A custom sensor system converts rider torso motions into direction and speed commands to control PURE, which is especially useful if a rider has minimal torso range of motion. We explored whether PURE's HF control performed as well as a traditional joystick (JS) human-robot interface and mWCUs, who may have reduced torso motion, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor environments. Each participant adjusted personal sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the effectiveness (number of collisions), efficiency (completion time), comfort (NASA TLX scores except physical demand), and robustness (index of performances) were similar for HF and JS control and between mWCUs and ABUs for all sections. These results suggest that PURE provides an effective method for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion. Seung Yun Song, Nadja Marin, Chenzhang Xiao, Mahshid Mansouri, João Ramos 0004, Yu Chen 0071, Adam Bleakney, Deana McDonagh, William R. Norris 0001, Jeannette Elliott, Patricia B. Malik, Elizabeth T. Hsiao-Wecksler |
HRI | 10 |
| 2023 | Design and Validation of a Torso-Dynamics Estimation System (TES) for Hands-Free Physical Human-Robot Interaction*abstractWe designed and validated two interfaces for physical human-robot interaction that utilize torso motions for hands-free navigation control of riding or remote mobile robots. The Torso-dynamics Estimation System (TES), which consisted of an instrumented seat (Force Sensing Seat, FSS) and a wearable sensor (inertial measurement unit, IMU), was developed to quantify the translational and rotational motions of the torso, respectively. The FSS was constructed from six uniaxial loadcells to output 3D resultant forces and torques, which were used to compute the translational movement of the 2D center of pressure (COP) under the seated user. Two versions of the FSS (Gen 1.0 and 2.0) with different loadcell layouts, materials, and manufacturing methods were developed to showcase the versatility of the FSS design and construction. Both FSS versions utilized low-cost components and a simple calibration protocol to correct for dimensional inaccuracies. The IMU, attached on the user’s upper chest, used a proprietary algorithm to compute the 3D torso angles without relying heavily on magnetometers to minimize errors from electromagnetic noises. A validation study was performed on eight test subjects (six able-bodied users and two manual wheelchair users with reduced torso range of motion) to validate TES estimations by comparing them to data collected on a research-grade force plate and motion capture system. TES readings displayed high accuracy (average RMSE of 3D forces, 3D torques, 2D COP, and torso angles were well less than maximum limits of 5N, 5Nm, 10mm, and 6°, respectively). Seung Yun Song, Yixiang Guo, Chentai Yuan, Nadja Marin, Chenzhang Xiao, Adam Bleakney, Jeannette Elliott, João Ramos 0004, Elizabeth T. Hsiao-Wecksler |
RO-MAN | 7 |