Fall Detection using Body Geometry in Video Sequences

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.

Authors:
Romaissa Beddiar Djamila, Mourad Oussalah, Brahim Nini, Yazid Bounab

Publication type:
A4 Article in conference proceedings

Place of publication:
2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)

Keywords:
Deep learning, Elderly assistance, Fall Detection, LSTM, Pretrained models, SVM classification

Published:

Full citation:
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

DOI:
https://doi.org/10.1109/IPTA50016.2020.9286456

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202102154788