Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evalu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Healthcare technology letters Jg. 3; H. 1; S. 6 - 11
Hauptverfasser: Baldewijns, Greet, Debard, Glen, Mertes, Gert, Vanrumste, Bart, Croonenborghs, Tom
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England The Institution of Engineering and Technology 01.03.2016
Schlagworte:
biomechanics
cameras
camera‐based fall detection algorithms
data integration
developed fall detection algorithms
fall incidents
geriatrics
health hazard
health hazards
highly realistic fall dataset
home setting
medical computing
real‐life data
simulated data
Special Issue: Decision Support for Person-Centred Healthcare
fall incidents
highly realistic fall dataset
developed fall detection algorithms
biomechanics
simulated data
cameras
geriatrics
home setting
camera-based fall detection algorithms
real-life data
health hazard
data integration
medical computing
health hazards
ISSN:2053-3713, 2053-3713
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2053-3713
2053-3713
DOI:10.1049/htl.2015.0047