TY - GEN
T1 - Snippet based trajectory statistics histograms for assistive technologies
AU - Iscen, Ahmet
AU - Wang, Yijie
AU - Duygulu, Pinar
AU - Hauptmann, Alex
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Action recognition
KW - Assisted living systems
KW - Medical device usage
UR - https://www.scopus.com/pages/publications/84925433082
U2 - 10.1007/978-3-319-16220-1_1
DO - 10.1007/978-3-319-16220-1_1
M3 - Conference contribution
AN - SCOPUS:84925433082
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 16
BT - Computer Vision - ECCV 2014 Workshops, Proceedings
A2 - Agapito, Lourdes
A2 - Bronstein, Michael M.
A2 - Rother, Carsten
PB - Springer Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -