Orkun Tunçel
Computer Vision and Pattern Recognition · 2010
A researcher in the Science corpus: 1 paper; trusts 22 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 17 citations into it; #11,702 of 21,575 by trust here, #18,266 by citations
- Biomedical Engineering — 13 citations into it; #3,684 of 10,734 by trust here, #9,032 by citations
- Physical Therapy, Sports Therapy and Rehabilitation — 6 citations into it
Whom Orkun Tunçel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- R. Golden
- Zsolt Miklós Kovács-Vajna
- Bijan Najafi
- Miikka Ermes
- Ling Bao
- Thomas Baltzer Moeslund
- A. G. Ramm
- Juha Pärkkä
- Stephen J. Preece
Who trusts Orkun Tunçel overall
Papers from Orkun Tunçel's perspective
Ordered by how strongly the researchers Orkun Tunçel trusts endorse each paper. Same corpus, a different order for every researcher.
- Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement (Physiological Measurement)
- Multi-sensor Activity Context Detection for Wearable Computing (EUSAI)
- Activity Recognition from User-Annotated Acceleration Data
- Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly (IEEE Transactions on Biomedical Engineering)
- Activity classification using realistic data from wearable sensors (IEEE Transactions on Information Technology in Biomedicine)
- Neural Networks: A Comprehensive Foundation
- Classification of basic daily movements using a triaxial accelerometer (Medical and Biological Engineering and Computing)
- Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring (IEEE Transactions on Information Technology in Biomedicine)
- Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions (IEEE Transactions on Information Technology in Biomedicine)
- Activity identification using body-mounted sensors—a review of classification techniques (Physiological Measurement)