Snippet based trajectory statistics histograms for assistive technologies

Date
2014-09
Advisor
Instructor
Source Title
European Conference on Computer Vision ECCV 2014: Computer Vision - ECCV 2014 Workshops
Print ISSN
Electronic ISSN
Publisher
Springer
Volume
Issue
Pages
3 - 16
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems. © Springer International Publishing Switzerland 2015.

Course
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Book Title
Keywords
Action recognition, Assisted living systems, Medical device usage, Biomedical equipment, Computer vision, Graphic methods, Hospitals, Interactive computer systems, Medical computing, Medical problems, Statistics, Trajectories, Action recognition, Assisted living, Assistive technology, Computer vision system, Human supervision, Medical Devices, Statistics histograms, Traveling time, Real time systems
Citation
Published Version (Please cite this version)