Statistical control chart and neural network classification for improving human fall detection
KAUST Grant NumberOSR-2015-CRG4-2582
Permanent link to this recordhttp://hdl.handle.net/10754/622645
MetadataShow full item record
AbstractThis paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow's fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.
CitationHarrou F, Zerrouki N, Sun Y, Houacine A (2016) Statistical control chart and neural network classification for improving human fall detection. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). Available: http://dx.doi.org/10.1109/ICMIC.2016.7804269.
SponsorsThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR- 2015-CRG4-2582.