Gert Mertes

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 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.

Automatic Fall Risk Estimation using the Nintendo Wii Balance Board

In this paper, a tool to assess a person´s fall risk with the Nintendo Wii Balance Board based on Center of Pressure (CoP) recordings is presented. Support Vector Machine and K-Nearest Neighbours classifiers are used to distinguish between people who experienced a fall in the past twelve months and those who have not. The classifiers are trained using data recorded from 39 people containing a mix of students and elderly. Validation is done using 10-fold cross-validation and the classifiers are also validated against additional data recorded from 12 elderly. A cross-validated average accuracy of 96.49% +/- 4.02 is achieved with the SVM classifier with radial basis function kernel and 95.72% +/- 1.48 is achieved with the KNN classifier with k=4. Validation against the additional dataset of 12 elderly results in a maximum accuracy of 76.6% with the linear SVM.