Neural network classifier for fall detection improved by Gram-Schmidt variable selection

The paper describes results of research on the fall detection in elderly residents based on infra red depth sensor measurements. We present the methodology of data acquisition, preprocessing and the feature extraction. Multilayer perceptron is used for classification. In order to improve the classifier generalization feature selection block by Gram-Schmidt orthogonalization is added. It determines the ranking of the features and enables to reduce the dimensionality of the data. Performance of our system measured in terms of sensitivity is 92% and precision is 93%, which means it can be used for real life applications.