Fall Detection and Activity Recognition Using Human Skeleton Features Articles uri icon



publication date

  • March 2021

start page

  • 33532

end page

  • 33542


  • 9

International Standard Serial Number (ISSN)

  • 2169-3536

Electronic International Standard Serial Number (EISSN)

  • 2169-3536


  • Human activity recognition has attracted the attention of researchers around the world. This
    is an interesting problem that can be addressed in different ways. Many approaches have been presented
    during the last years. These applications present solutions to recognize different kinds of activities such as if
    the person is walking, running, jumping, jogging, or falling, among others. Amongst all these activities, fall
    detection has special importance because it is a common dangerous event for people of all ages with a more
    negative impact on the elderly population. Usually, these applications use sensors to detect sudden changes
    in the movement of the person. These kinds of sensors can be embedded in smartphones, necklaces, or smart
    wristbands to make them ‘‘wearable"" devices. The main inconvenience is that these devices have to be
    placed on the subjects" bodies. This might be uncomfortable and is not always feasible because this type
    of sensor must be monitored constantly, and can not be used in open spaces with unknown people. In this
    way, fall detection from video camera images presents some advantages over the wearable sensor-based
    approaches. This paper presents a vision-based approach to fall detection and activity recognition. The main
    contribution of the proposed method is to detect falls only by using images from a standard video-camera
    without the need to use environmental sensors. It carries out the detection using human skeleton estimation
    for features extraction. The use of human skeleton detection opens the possibility for detecting not only falls
    but also different kind of activities for several subjects in the same scene. So this approach can be used in real
    environments, where a large number of people may be present at the same time. The method is evaluated with
    the UP-FALL public dataset and surpasses the performance of other fall detection and activities recognition
    systems that use that dataset


  • fall detection; deep learning; human skeleton