Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sean J. Keller
dblp:264/7298
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2Human-computer interaction and ubiquitous computing · 2
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
3 papers |
Haptics and multimodal interaction · 57% Wearable and physiological sensing · 24% Personal fabrication and tangible interfaces · 16% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Haptics and multimodal interaction
haptic rendering |
0.7 | 2 | 2020 | PneuSleeve: In-fabric Multimodal Actuation and Sensing in a Soft, Compact, and Expressive Haptic Sleeve · CHI 2020 Comparison of kinesthetic and skin deformation feedback for mass rendering · ICRA 2016 |
Personal fabrication and tangible interfaces
pneumatic actuation |
0.4 | 1 | 2020 | PneuSleeve: In-fabric Multimodal Actuation and Sensing in a Soft, Compact, and Expressive Haptic Sleeve · CHI 2020 |
Haptics and multimodal interaction
tactile display |
0.4 | 1 | 2020 | PneuSleeve: In-fabric Multimodal Actuation and Sensing in a Soft, Compact, and Expressive Haptic Sleeve · CHI 2020 |
Haptics and multimodal interaction
haptic feedback |
0.2 | 1 | 2016 | Comparison of kinesthetic and skin deformation feedback for mass rendering · ICRA 2016 |
Wearable and physiological sensing
magnetic sensing |
0.2 | 1 | 2016 | Finexus: Tracking Precise Motions of Multiple Fingertips Using Magnetic Sensing · CHI 2016 |
Haptics and multimodal interaction › haptic feedback
skin deformation feedback |
0.2 | 1 | 2016 | Comparison of kinesthetic and skin deformation feedback for mass rendering · ICRA 2016 |
Virtual and augmented reality
immersive interaction |
0.1 | 1 | 2016 | Finexus: Tracking Precise Motions of Multiple Fingertips Using Magnetic Sensing · CHI 2016 |
Immersive interaction
virtual reality |
0.1 | 1 | 2016 | Comparison of kinesthetic and skin deformation feedback for mass rendering · ICRA 2016 |
Methods — techniques the papers use, named apart from their topics
3d position estimation · 0.5user study · 0.4psychophysical evaluation · 0.4closed-loop control · 0.4psychophysical experiment · 0.2method of adjustments · 0.2bandpass filtering · 0.2band-pass filtering · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | PneuSleeve: In-fabric Multimodal Actuation and Sensing in a Soft, Compact, and Expressive Haptic SleeveabstractIntegration of soft haptic devices into garments can improve their usability and wearability for daily computing interactions. In this paper, we introduce PneuSleeve, a fabric-based, compact, and highly expressive forearm sleeve which can render a broad range of haptic stimuli including compression, skin stretch, and vibration. The haptic stimuli are generated by controlling pneumatic pressure inside embroidered stretchable tubes. The actuation configuration includes two compression actuators on the proximal and distal forearm, and four uniformly distributed linear actuators around and tangent to the forearm. Further, to ensure a suitable grip force, two soft mutual capacitance sensors are fabricated and integrated into the compression actuators, and a closed-loop force controller is implemented. We physically characterize the static and dynamic behavior of the actuators, as well as the performance of closed-loop control. We quantitatively evaluate the psychophysical characteristics of the six actuators in a set of user studies. Finally, we show the expressiveness of PneuSleeve by evaluating combined haptic stimuli using subjective assessments. Mengjia Zhu, Amirhossein H. Memar, Aakar Gupta, Majed Samad, Priyanshu Agarwal, Yon Visell, Sean J. Keller, Nick Colonnese |
CHI | 7 |
| 2017 | Development and validation of modeling framework for interconnected tendon networks in robotic and human fingersabstractThe primary contribution of this work is the development of a generalized modeling methodology for complex tendon systems toward a long-term goal of modeling the mechanical structure of the human finger. The key feature of this model is its ability to predict how muscle forces will transmit through an interconnected tendon network based on tendon kinematics and the current joint posture, so that the transformation from input muscle forces to output joint torques and fingertip forces is accurately represented. The feasibility of this model is evaluated by using a tendon-driven robotic finger testbed. Moreover, we utilize the validated model to explore unique features of the extensor mechanism. Taylor Niehues, Raymond J. King, Ashish D. Deshpande, Sean J. Keller |
ICRA | 4 |
| 2016 | Finexus: Tracking Precise Motions of Multiple Fingertips Using Magnetic SensingabstractWith the resurgence of head-mounted displays for virtual reality, users need new input devices that can accurately track their hands and fingers in motion. We introduce Finexus, a multipoint tracking system using magnetic field sensing. By instrumenting the fingertips with electromagnets, the system can track fine fingertip movements in real time using only four magnetic sensors. To keep the system robust to noise, we operate each electromagnet at a different frequency and leverage bandpass filters to distinguish signals attributed to individual sensing points. We develop a novel algorithm to efficiently calculate the 3D positions of multiple electromagnets from corresponding field strengths. In our evaluation, we report an average accuracy of 1.33 mm, as compared to results from an optical tracker. Our real-time implementation shows Finexus is applicable to a wide variety of human input tasks, such as writing in the air. Ke-Yu Chen, Shwetak N. Patel, Sean J. Keller |
CHI | 3 |
| 2016 | Comparison of kinesthetic and skin deformation feedback for mass renderingabstractVirtual and augmented reality systems that immerse users in a 3D environment could benefit from haptic (force and/or tactile) feedback to increase realism and task performance. A canonical task that cannot be achieved compellingly without haptic feedback is the grasp and lift of an object with mass. Traditional kinesthetic (force) feedback devices provide realistic physical interactions with virtual objects, but typically require large actuators, making them bulky and encumbering. In contrast, tactile feedback devices can use smaller actuators and may enable freer movement of a user in a virtual world. One form of tactile feedback, skin deformation feedback, has been previously shown to provide effective haptic feedback of force, stiffness, and friction. Here we compare human perception of mass via kinesthetic feedback and skin deformation feedback in a virtual environment. Participants grasp and lift two virtual blocks and attempt to equalize their masses by the method of adjustments. From the accuracy of this equalization, we determined that the Weber Fraction (the just noticeable difference in proportion to the original stimulus value) of virtual mass during a grasp and lift task was 0.11 for kinesthetic force feedback and 0.35 for skin deformation feedback. In addition, participants exhibited differences in exploratory procedures between the two types of feedback. Jacob M. Suchoski, Aaron C. Barron, Connie Wu, Zhan Fan Quek, Sean J. Keller, Allison M. Okamura |
ICRA | 5 |