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Kevin Caves

dblp:37/9841 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0001-6630-9615ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorComputer networks · 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
1 paper
Interaction techniques and input · 77% Ubiquitous computing and smart environments · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › text entry
gesture-based text entry
0.112011
Using mobile phones to write in air · MobiSys 2011
Ubiquitous computing and smart environments
mobile sensing
0.012011
Using mobile phones to write in air · MobiSys 2011

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

accelerometer-based gesture recognition · 0.1
YearPublicationVenuePosition
2020 Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations
Leighanne Jarvis, Sarah Moninger, Chandra Throckmorton, Juliessa Pavon, Kevin Caves
ICCHP (2)5
2018 Development and Evaluation of a Device to Measure Walking Speed
Kevin Caves, Leighanne Jarvis, Frank DeRuyter
ICCHP (2)1
2011 Using mobile phones to write in air
abstract
Numerous sensors in modern mobile phones enable a range of people-centric applications. This paper envisions a system called PhonePoint Pen that uses the in-built accelerometer in mobile phones to recognize human writing. By holding the phone like a pen, a user should be able to write short messages or draw simple diagrams in the air. The acceleration due to hand gestures can be translated into geometric strokes, and recognized as characters. We prototype the PhonePoint Pen on the Nokia N95 platform, and evaluate it through real users. Results show that English characters can be identified with an average accuracy of 91.9%, if the users conform to a few reasonable constraints. Future work is focused on refining the prototype, with the goal of offering a new user-experience that complements keyboards and touch-screens.
Sandip Agrawal, Ionut Constandache, Shravan Gaonkar, Romit Roy Choudhury, Kevin Caves, Frank DeRuyter
MobiSys5