Publication: Human Activity Recognition by Sequences of Skeleton Features
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2021-06
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MDPI AG
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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.
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Keywords
Fall detection, Activity recognition, Machine learning, Human skeleton, Images sequence
Bibliographic citation
Ramirez, H., Velastin, S. A., Aguayo, P., Fabregas, E., & Farias, G. (2022). Human Activity Recognition by Sequences of Skeleton Features. In Sensors (Vol. 22, Issue 11, p. 3991). MDPI AG.