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...

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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
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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.
AbstractList	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.
Author	Debard, Glen Mertes, Gert Croonenborghs, Tom Vanrumste, Bart Baldewijns, Greet
AuthorAffiliation	1 KU Leuven Technology Campus Geel , AdvISe , Geel , Belgium 2 KU Leuven , ESAT-STADIUS , Leuven , Belgium 3 iMinds Medical Information Technology Department , Gent , Belgium 4 Thomas More Kempen , Mobilab , Geel , Belgium 6 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 5 Department of Computer Science, DTAI , KU Leuven , Leuven , Belgium
AuthorAffiliation_xml	– name: 6 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 – name: 5 Department of Computer Science, DTAI , KU Leuven , Leuven , Belgium – name: 3 iMinds Medical Information Technology Department , Gent , Belgium – name: 4 Thomas More Kempen , Mobilab , Geel , Belgium – name: 1 KU Leuven Technology Campus Geel , AdvISe , Geel , Belgium – name: 2 KU Leuven , ESAT-STADIUS , Leuven , Belgium
Author_xml	– sequence: 1 givenname: Greet surname: Baldewijns fullname: Baldewijns, Greet email: greet.baldewijns@kuleuven.be organization: 3iMinds Medical Information Technology Department, Gent, Belgium – sequence: 2 givenname: Glen surname: Debard fullname: Debard, Glen organization: 4Thomas More Kempen, Mobilab, Geel, Belgium – sequence: 3 givenname: Gert surname: Mertes fullname: Mertes, Gert organization: 3iMinds Medical Information Technology Department, Gent, Belgium – sequence: 4 givenname: Bart surname: Vanrumste fullname: Vanrumste, Bart organization: 3iMinds Medical Information Technology Department, Gent, Belgium – sequence: 5 givenname: Tom surname: Croonenborghs fullname: Croonenborghs, Tom organization: 6Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital, Harvard Medical School, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
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SubjectTerms	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
Title	Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms
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