Deep learning classifier for fall detection based on IR distance sensor data

The goal of research is the fall detection in elderly residents based on infra red depth sensor measurements. Our attention is focused on statistical properties as generalization. The effectiveness of discriminative statistical classifiers (multilayer perceptron) is improved by addition of feature selection block by Gram-Schmidt orthogonalization, which determines the ranking of the features, and NPCA block, which transforms the raw data into a nonlinear manifold and reduces 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.