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Alanson Sample

dblp:424/4308 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-8046-0538ORCID · corroborated

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

Computer networks · 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
Wearable and physiological sensing · 77% Health and well-being technologies · 23%
Computer networks
1 paper
Wireless sensing and localization · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
magnetic sensing
1.012026
μTouch: Enabling Accurate, Lightweight Self-Touch Sensing with Passive Magnets · PerCom 2026
Health and well-being technologies › health monitoring
hygiene monitoring
0.312026
μTouch: Enabling Accurate, Lightweight Self-Touch Sensing with Passive Magnets · PerCom 2026
Wireless sensing and localization
magnetic sensing
0.312026
MagLens: Bringing Mobile, Fine-Grained Imaging to Ferrous Building Structures · SenSys 2026

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

semi-supervised learning · 1.0magnetometer · 1.0magnetic imaging · 1.0fine-grained sensing · 1.0
YearPublicationVenuePosition
2026 μTouch: Enabling Accurate, Lightweight Self-Touch Sensing with Passive Magnets
abstract
Self-touch gestures (e.g., nuanced facial touches and subtle finger scratches) provide rich insights into human behaviors, from hygiene practices to health monitoring. However, existing approaches fall short in detecting such micro gestures due to their diverse movement patterns.This paper presents μTouch, a novel magnetic sensing platform for self-touch gesture recognition. μTouch features (1) a compact hardware design with low-power magnetometers and magnetic silicon, (2) a lightweight semi-supervised framework requiring minimal user data, and (3) an ambient field detection module to mitigate environmental interference. We evaluated μTouch in two representative applications in user studies with 11 and 12 participants. μTouch only requires three-second fine-tuning data for each gesture — new users need less than one minute before starting to use the system. μTouch can distinguish eight different face-touching behaviors with an average accuracy of 93.41%, and reliably detect body-scratch behaviors with an average accuracy of 94.63%. μTouch demonstrates accurate and robust sensing performance even after a month, showcasing its potential as a practical tool for hygiene monitoring and dermatological health applications.
Ke Li 0013, Jike Wang, Cheng Zhang 0022, Alanson Sample, Dongyao Chen
PerCom6
2026 MagLens: Bringing Mobile, Fine-Grained Imaging to Ferrous Building Structures
abstract
Fine-grained inspection of ferrous structures, such as steel rebars and iron pipes, is essential for ensuring structural health/integrity. However, existing non-destructive imaging techniques often suffer from coarse spatial resolution, high operational costs, and limited mobility support, hence severely restricting their practical deployment. For example, ground-penetrating radar (GPR), constrained by its operating wavelength, cannot resolve sub-centimeter features or recover fine contours of embedded ferrous structures.
Jike Wang, Yasha Iravantchi, Mingke Wang, Alanson Sample, Kang G. Shin, Xinbing Wang, Dongyao Chen
SenSys4