Citation

Baldewijns G, Debard G, Mertes G, Vanrumste B, Croonenborghs T. Healthc. Technol. Lett. 2016; 3(1): 6-11.

Affiliation

KU Leuven Technology Campus Geel, AdvISe, Geel, Belgium; Department of Computer Science, DTAI, KU Leuven, Leuven, Belgium; Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital, Harvard Medical School, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.

Copyright

(Copyright © 2016, Institution of Engineering and Technology)

DOI

10.1049/htl.2015.0047

PMID

27222726

Abstract

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.

Language: en