Human Activity Recognition by Sequences of Skeleton Features Articles uri icon

authors

  • Ramirez, Heilym
  • VELASTIN CARROZA, SERGIO ALEJANDRO
  • AGUAYO, PAULO
  • FABREGAS, ERNESTO
  • FARIAS, GONZALO

publication date

  • June 2021

start page

  • 1

end page

  • 21

issue

  • 11

volume

  • 22

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

abstract

  • In recent years, much effort has been devoted to the development of applications capable
    of detecting different types of human activity. In this field, fall detection is particularly relevant,
    especially for the elderly. On the one hand, some applications use wearable sensors that are integrated
    into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing
    the device. The main drawback of these types of systems is that these devices must be placed
    on a person"s body. This is a major drawback because they can be uncomfortable, in addition to
    the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In
    contrast, other approaches perform activity recognition from video camera images, which have many
    advantages over the previous ones since the user is not required to wear the sensors. As a result, these
    applications can be implemented in open spaces and with unknown people. This paper presents a
    vision-based algorithm for activity recognition. The main contribution of this work is to use human
    skeleton pose estimation as a feature extraction method for activity detection in video camera images.
    The use of this method allows the detection of multiple people"s activities in the same scene. The
    algorithm is also capable of classifying multi-frame activities, precisely for those that need more than
    one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared
    to similar algorithms using the same dataset.

subjects

  • Telecommunications

keywords

  • fall detection; activity recognition; machine learning; human skeleton; images sequence