Demo Data from SIGIR 2026 / reviewers in the wild / expert
David Rempel
dblp:96/1235
· DBLP profile ↗
6ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-6472-6500ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 4 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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
2 papers |
Interaction techniques and input · 97% Accessibility and assistive technology · 3% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › gesture input
hand gestures |
0.2 | 1 | 2014 | The design of hand gestures for human-computer interaction: Lessons from sign language interpreters · Int. J. Hum. Comput. Stud. 2014 |
Interaction techniques and input
gesture |
0.1 | 1 | 2014 | The design of hand gestures for human-computer interaction: Lessons from sign language interpreters · Int. J. Hum. Comput. Stud. 2014 |
Interaction techniques and input › input device
keyboard design |
0.0 | 1 | 1992 | Toward a more humane keyboard · CHI 1992 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Professional and High-Level Gamers: Differences in Performance, Muscle Activity, and Hand Kinematics for Different MiceabstractComputer mouse design can impact user comfort and performance. The effect of mouse design on gamers, who use a mouse for long hours and apply higher velocity movements than office workers, is uncertain. Professional (N = 29) and high-level (N = 19) gamers participated in this laboratory study and performed Fitts’ and gaming tasks (OverwatchTM) with different mice, and this analysis compared results from a light-weight (87 g) wireless mouse and a very light-weight (80 g) wireless mouse. There was little difference between the mice on muscle activity, hand motion, usability, or fatigue; however, professional gamers preferred the 80 g mouse. Professional gamers achieved higher levels of throughput and lower levels of path deviation than high-level gamers and their hand movement velocities and accelerations were significantly higher (p<0.05). The observed hand movement patterns used by professionals may be useful for instructing other gamers on techniques to improve performance. Guangchuan Li, Mengcheng Wang, Federico Arippa, Alan Barr, David Rempel, Yue Liu 0005, Carisa Harris-Adamson |
Int. J. Hum. Comput. Interact. | 5 |
| 2019 | The Design of Hand Gestures for Selecting Virtual ObjectsabstractProviding input for the selection of objects in virtual, augmented, and mixed-mode reality can be done with hand-held controllers or hand gestures depending on the complexity and precision required. Free hand gestures have the advantages of eliminating the need for a controller, not needing to see the controls on the controller, and potentially being less fatiguing. However, the designs of hand gestures for object selection have not been thoroughly evaluated for usability and performance. Eighteen participants evaluated four different ray-casting hand gestures (index thrust, index click, palm thrust, and palm click) and three snapback thresholds while selecting 2D targets of different sizes. Dependent variables were mean time to select targets, number of selections not completed, number of incorrect targets selected, and subjective preference. The index thrust and index click gestures were preferred by subjects and had faster mean selection times and lower number of incorrect target selections. There were no significant differences between snapback thresholds on productivity, error, or preference. Overall, the index thrust and index click gestures were associated with greater user confidence, particularly with smaller targets. Jeffrey Lin, Carisa Harris-Adamson, David Rempel |
Int. J. Hum. Comput. Interact. | 3 |
| 2019 | Design of finger gestures for locomotion in virtual realityabstractBackground Within a virtual environment (VE) the control of locomotion (e.g., self-travel) is critical for creating a realistic and functional experience. Usually the direction of locomotion, while using a head-mounted display (HMD), is determined by the direction the head is pointing and the forward or backward motion is controlled with a hand held controllers. However, hand held devices can be difficult to use while the eyes are covered with a HMD. Free hand gestures, that are tracked with a camera or a hand data glove, have an advantage of eliminating the need to look at the hand controller but the design of hand or finger gestures for this purpose has not been well developed. Methods This study used a depth-sensing camera to track fingertip location (curling and straightening the fingers), which was converted to forward or backward self-travel in the VE. Fingertip position was converted to self-travel velocity using a mapping function with three parameters: a region of zero velocity (dead zone) around the relaxed hand position, a linear relationship of fingertip position to velocity (slope or/J) beginning at the edge of the dead zone, and an exponential relationship rather than a linear one mapping fingertip position to velocity (exponent). Using a HMD, participants moved forward along a virtual road and stopped at a target on the road by controlling self-travel velocity with finger flexion and extension. Each of the 3 mapping function parameters was tested at 3 levels. Outcomes measured included usability ratings, fatigue, nausea, and time to complete the tasks. Results Twenty subjects participated but five did not complete the study due to nausea. The size of the dead zone had little effect on performance or usability. Subjects preferred lower β values which were associated with better subjective ratings of control and reduced time to complete the task, especially for large targets. Exponent values of 1.0 or greater were preferred and reduced the time to complete the task, especially for small targets. Conclusions Small finger movements can be used to control velocity of self-travel in VE. The functions used for converting fingertip position to movement velocity influence usability and performance. Rachel Huang, Carisa Harris-Adamson, Dan Odell, David Rempel |
Virtual Real. Intell. Hardw. | 4 |
| 2017 | Fast hand posture classification using depth features extracted from random line segments
Weizhi Nai, Yue Liu 0005, David Rempel, Yongtian Wang |
Pattern Recognit. | 3 |
| 2014 | The design of hand gestures for human-computer interaction: Lessons from sign language interpreters
David Rempel, Matt J. Camilleri, David L. Lee |
Int. J. Hum. Comput. Stud. | 1 |
| 1992 | Toward a more humane keyboardabstractArticle Free Access Share on Toward a more humane keyboard Authors: William Hargreaves View Profile , David Rempel View Profile , Nachman Halpern View Profile , Robert Markison View Profile , Karl Kroemer View Profile , Ing View Profile , Jack Litewka View Profile Authors Info & Claims CHI '92: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsJune 1992 Pages 365–368https://doi.org/10.1145/142750.142832Online:01 June 1992Publication History 1citation807DownloadsMetricsTotal Citations1Total Downloads807Last 12 Months36Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF William Hargreaves, David Rempel, Nachman Halpern, Robert Markison, Karl Kroemer, Jack Litewka |
CHI | 2 |