J
Jamie A. Ward
Researcher at Goldsmiths, University of London
Publications - 54
Citations - 3125
Jamie A. Ward is an academic researcher from Goldsmiths, University of London. The author has contributed to research in topics: Computer science & Wearable computer. The author has an hindex of 14, co-authored 42 publications receiving 2816 citations. Previous affiliations of Jamie A. Ward include École Polytechnique Fédérale de Lausanne & University of London.
Papers
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Journal ArticleDOI
AMON: a wearable multiparameter medical monitoring and alert system
U. Anliker,Jamie A. Ward,Paul Lukowicz,Gerhard Tröster,F. Dolveck,M. Baer,F. Keita,Eran Schenker,F. Catarsi,L. Coluccini,Andrea Belardinelli,D. Shklarski,M. Alon,E. Hirt,R. Schmid,M. Vuskovic +15 more
TL;DR: The AMON system includes continuous collection and evaluation of multiple vital signs, intelligent multiparameter medical emergency detection, and a cellular connection to a medical center, and is validated by a medical study with a set of 33 subjects.
Journal ArticleDOI
Eye Movement Analysis for Activity Recognition Using Electrooculography
TL;DR: The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
Journal ArticleDOI
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
TL;DR: This work describes a method for the recognition of activities that are characterized by a hand motion and an accompanying sound using microphones and three-axis accelerometers mounted at two positions on the user's arms using on-body sensing.
Book ChapterDOI
Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers
Paul Lukowicz,Jamie A. Ward,Holger Junker,Mathias Stäger,Gerhard Tröster,Amin Atrash,Thad Starner +6 more
TL;DR: In this article, the authors presented a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors based on a novel way of combining data from accelerometers with simple frequency matching sound classification.
Journal ArticleDOI
Performance metrics for activity recognition
TL;DR: A comprehensive set of performance metrics and visualisations for continuous activity recognition (AR) and shows that where event- and frame-based precision and recall lead to an ambiguous interpretation of results in some cases, the proposed metrics provide a consistently unambiguous explanation.