Improving the accuracy of existing camera based fall detection algorithms through late fusion

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this p...

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Published in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2017; pp. 2667 - 2671
Main Authors: Baldewijns, Greet, Debard, Glen, Mertes, Gert, Croonenborghs, Tom, Vanrumste, Bart
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.07.2017
Subjects:
Cameras
Detection algorithms
Detectors
Hospitals
Noise measurement
Support vector machines
Three-dimensional displays
ISSN:1557-170X, 2694-0604, 2694-0604
Online Access:Get full text
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Summary:Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.
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ISSN:1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2017.8037406