Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Shan Xu 0004

dblp:22/6890-4 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0001-7796-3022ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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
Interaction techniques and input · 61% Haptics and multimodal interaction · 30% Accessibility and assistive technology · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input
gesture input
0.812024
Designing Haptic Feedback for Sequential Gestural Inputs · CHI 2024
Haptics and multimodal interaction › haptic feedback
haptic feedback design
0.812024
Designing Haptic Feedback for Sequential Gestural Inputs · CHI 2024
Interaction techniques and input › gesture input
mid-air gestures
0.812024
Designing Haptic Feedback for Sequential Gestural Inputs · CHI 2024
Accessibility and assistive technology
older adults
0.212024
Designing Haptic Feedback for Sequential Gestural Inputs · CHI 2024

Methods — techniques the papers use, named apart from their topics

user study · 0.8
YearPublicationVenuePosition
2025 Mechanoreceptive Aβ Primary Afferents Discriminate Naturalistic Social Touch Inputs at a Functionally Relevant Time Scale
abstract
Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered social touch expressions. Leveraging statistical and classification analyses, we found that single units of multiple mechanoreceptive Aβ subtypes, especially slowly adapting type II (SA-II) and fast adapting hair follicle afferents (HFA), can reliably differentiate social touch expressions at accuracies similar to human recognition. We then identified the most informative firing patterns of SA-II and HFA afferents, which indicate that average durations of 3-4 s of firing provide sufficient discriminative information. Those two subtypes also exhibit robust tolerance to spike-timing shifts of up to 10-20 ms, varying with touch expressions due to their specific firing properties. Greater shifts in spike-timing, however, can change a firing pattern's envelope to resemble that of another expression and drastically compromise an afferent's discrimination capacity. Altogether, the findings indicate that SA-II and HFA afferents differentiate the skin contact of social touch at time scales relevant for such interactions, which are 1-2 orders of magnitude longer than those for non-social touch.
Shan Xu 0004, Steven Conrad Hauser, Saad S. Nagi, James A. Jablonski, Merat Rezaei, Ewa Jarocka, Andrew G. Marshall, Håkan Olausson, Sarah McIntyre, Gregory J. Gerling
IEEE Trans. Affect. Comput.1
2024 Designing Haptic Feedback for Sequential Gestural Inputs
abstract
This work seeks to design and evaluate haptic feedback for sequential gestural inputs, where mid-air hand gestures are used to express system commands. Nine haptic patterns are first designed leveraging metaphors. To pursue efficient interaction, we examine the trade-off between pattern duration and recognition accuracy and find that durations as short as 0.3s-0.5s achieve roughly 80%-90% accuracy. We then examine the haptic design for sequential inputs, where we vary when the feedback for each gesture is provided, along with pattern duration, gesture sequence length, and age. Results show that providing haptic patterns right after detected hand gestures leads to significantly more efficient interaction compared with concatenating all haptic patterns after the gesture sequence. Moreover, the number of gestures had little impact on performance, but age is a significant predictor. Our results suggest that immediate feedback with 0.3s and 0.5s pattern duration would be recommended for younger and older users respectively.
Shan Xu 0004, Sarah Sykes, Parastoo Abtahi, Tovi Grossman, Daylon Walden, Michael Glueck, Carine Rognon
CHI1