@Article{AlbertClapes2018,
author="Albert Clapes
and Alex Pardo
and Oriol Pujol
and Sergio Escalera",
title="Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly",
journal="Machine Vision and Applications",
year="2018",
volume="29",
number="5",
pages="765--788",
optkeywords="Multimodal activity detection",
optkeywords="Computer vision",
optkeywords="Inertial sensors",
optkeywords="Dense trajectories",
optkeywords="Dynamic time warping",
optkeywords="Assistive technology",
abstract="We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF-  2  kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user{\textquoteright}s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3125), last updated on Mon, 24 Jan 2022 17:28:02 +0100",
opturl="https://link.springer.com/article/10.1007/s00138-018-0931-1",
file=":http://158.109.8.37/files/CPP2018.pdf:PDF"
}