University of Oulu

B. Djamila Romaissa, O. Mourad, N. Brahim and B. Yazid, "Fall Detection using Body Geometry in Video Sequences," 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2020, pp. 1-5, doi: 10.1109/IPTA50016.2020.9286456

Fall detection using body geometry in video sequences

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Author: Romaissa, Beddiar Djamila1,2; Mourad, Oussalah2; Brahim, Nini1;
Organizations: 1Research Laboratory on Computer Science’s Complex Systems University Laarbi Ben M’hidi, Oum El Bouaghi, Algeria
2Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-15


According to the World Health Organization, falling of the elderly is a major health problem that causes many injuries and thousands of deaths every year. This increases pressure on health authorities to provide daily health care, reliable medical assistance, reduce fall damages and improve the elderly quality of life. For that, it is a priority to detect or predict falls accurately. In this paper, we present a fall detection approach based on human body geometry inferred from video sequence frames. We calculate the angular information between the vector formed by the head centroid of the identified facial image and the center hip of the body and the vector aligned with the horizontal axis of the center hip. Similarly, we calculate the distance between the vector formed by the head and the body center hip and the vector formed on its horizontal axis; we then construct distinctive image features. These angles and distances are then used to train a two-class SVM classifier and a Long Short-Term Memory network (LSTM) on the calculated angle sequences to classify falls and no-falls activities. We perform experiments on the Le2i fall detection dataset. The results demonstrate the effectiveness and efficiency of the developed approach.

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Series: Proceedings. International Workshops on Image Processing Theory, Tools, and Applications
ISSN: 2154-5111
ISSN-E: 2154-512X
ISSN-L: 2154-5111
ISBN: 978-1-7281-8750-1
ISBN Print: 978-1-7281-8751-8
Pages: 1 - 5
Article number: 9286456
DOI: 10.1109/IPTA50016.2020.9286456
Host publication: 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Conference: International Conference on Image Processing Theory, Tools and Applications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work is partly supported by the Algerian Residential Training Program Abroad Outstanding National Program (PNE) that supported the first author stay at University of Oulu and European YougRes project (Ref. 823701), which are gratefully acknowledged.
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