T
Thad Starner
Researcher at Georgia Institute of Technology
Publications - 392
Citations - 25980
Thad Starner is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Wearable computer & Gesture. The author has an hindex of 72, co-authored 376 publications receiving 24485 citations. Previous affiliations of Thad Starner include Massachusetts Institute of Technology & Electronics and Telecommunications Research Institute.
Papers
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Journal ArticleDOI
Energy scavenging for mobile and wireless electronics
Joseph A. Paradiso,Thad Starner +1 more
TL;DR: A whirlwind survey of energy harvesting can be found in this article, where the authors present a survey of recent advances in energy harvesting, spanning historic and current developments in sensor networks and mobile devices.
Journal ArticleDOI
Real-time American sign language recognition using desk and wearable computer based video
TL;DR: Two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands are presented.
Journal ArticleDOI
Using GPS to learn significant locations and predict movement across multiple users
Daniel Ashbrook,Thad Starner +1 more
TL;DR: This work presents a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales and incorporates these locations into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
Book ChapterDOI
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
Cory D. Kidd,Robert J. Orr,Gregory D. Abowd,Christopher G. Atkeson,Irfan Essa,Blair MacIntyre,Elizabeth D. Mynatt,Thad Starner,Wendy C. Newstetter +8 more
TL;DR: The Aware Home project is introduced and some of the technology-and human-centered research objectives in creating the Aware Home are outlined, to create a living laboratory for research in ubiquitous computing for everyday activities.
Proceedings ArticleDOI
Real-time American Sign Language recognition from video using hidden Markov models
Thad Starner,Alex Pentland +1 more
TL;DR: A real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.