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Enrique Garcia-Ceja
Researcher at SINTEF
Publications - 42
Citations - 1246
Enrique Garcia-Ceja is an academic researcher from SINTEF. The author has contributed to research in topics: Activity recognition & Computer science. The author has an hindex of 12, co-authored 40 publications receiving 662 citations. Previous affiliations of Enrique Garcia-Ceja include University of Oslo & Monterrey Institute of Technology and Higher Education.
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Journal ArticleDOI
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Hanna Borgli,Vajira Thambawita,Pia H. Smedsrud,Steven Alexander Hicks,Debesh Jha,Sigrun Losada Eskeland,Kristin Ranheim Randel,Konstantin Pogorelov,Mathias Lux,Duc Tien Dang Nguyen,Dag Johansen,Carsten Griwodz,Håkon Kvale Stensland,Håkon Kvale Stensland,Enrique Garcia-Ceja,Peter T. Schmidt,Hugo Lewi Hammer,Michael Riegler,Pål Halvorsen,Thomas de Lange +19 more
TL;DR: The HyperKvasir dataset is presented, the largest image and video dataset of the gastrointestinal tract available today and can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
Journal ArticleDOI
Mental health monitoring with multimodal sensing and machine learning: A survey
Enrique Garcia-Ceja,Michael Riegler,Tine Nordgreen,Tine Nordgreen,Petter Jakobsen,Petter Jakobsen,Ketil J. Oedegaard,Ketil J. Oedegaard,Jim Torresen +8 more
TL;DR: A classification taxonomy is proposed to guide the review of related works and present the overall phases of MHMS, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on.
Journal ArticleDOI
Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step
TL;DR: Data from the smartphone's built-in accelerometer is used to detect behavior that correlates with subjects stress levels, and a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models are achieved, relying solely on data from a single accelerometer.
Journal ArticleDOI
Multi-view stacking for activity recognition with sound and accelerometer data
TL;DR: This work proposes the use of a multi-view stacking method to fuse the data from heterogeneous types of sensors for activity recognition, and uses sound and accelerometer data collected with a smartphone and a wrist-band while performing home task activities.
Proceedings ArticleDOI
Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients
Enrique Garcia-Ceja,Michael Riegler,Petter Jakobsen,Jim Torresen,Tine Nordgreen,Ketil J. Oedegaard,Ole Bernt Fasmer +6 more
TL;DR: A unique dataset containing sensor data collected from patients suffering from depression, which contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls is presented.