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Andrew Doan

dblp:344/9957 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-9096-9530ORCID · reported

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

Human-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
Personal fabrication and tangible interfaces · 56% Interaction techniques and input · 44%

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

TopicWeightPapersLastEvidence papers
Personal fabrication and tangible interfaces › digital fabrication
3d printed mechanisms
0.712023
MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms · CHI 2023
Interaction techniques and input › input sensing
capacitive sensing
0.712023
MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms · CHI 2023
Personal fabrication and tangible interfaces
tangible user interface
0.212023
MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms · CHI 2023

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

computational model for print deviation compensation · 0.7capacitive sensing · 0.73d printing · 0.7
YearPublicationVenuePosition
2023 MechSense: A Design and Fabrication Pipeline for Integrating Rotary Encoders into 3D Printed Mechanisms
abstract
We introduce MechSense, 3D-printed rotary encoders that can be fabricated in one pass alongside rotational mechanisms, and report on their angular position, direction of rotation, and speed. MechSense encoders utilize capacitive sensing by integrating a floating capacitor into the rotating element and three capacitive sensor patches in the stationary part of the mechanism. Unlike existing rotary encoders, MechSense does not require manual assembly but can be seamlessly integrated during design and fabrication. Our MechSense editor allows users to integrate the encoder with a rotating mechanism and exports files for 3D-printing. We contribute a sensor topology and a computational model that can compensate for print deviations. Our technical evaluation shows that MechSense can detect the angular position (mean error: 1.4°) across multiple prints and rotations, different spacing between sensor patches, and different sizes of sensors. We demonstrate MechSense through three application examples on 3D-printed tools, tangible UIs, and gearboxes.
Marwa Alalawi, Noah Pacik-Nelson, Junyi Zhu 0001, Ben Greenspan, Andrew Doan, Brandon M. Wong, Benjamin Owen-Block, Shanti Kaylene Mickens, Wilhelm Jacobus Schoeman, Michael Wessely, Andreea Danielescu 0001, Stefanie Mueller 0001
CHI5