Subject Filtering for Passive Biometric Monitoring

Biometrie data can provide useful information about the person’s overall wellness. However, the invasiveness of the data collection process often prevents their wider exploitation. To alleviate this difficulty we are developing a biométrie monitoring system that relies on nonintrusive biological traits such as speech and gait. We report on the development of the pattern recognition module of the system that is used to filter out nonsubject data. Our system builds upon a number of signal processing and statistical machine learning techniques to process and filter the data, including, Principal Component Analysis for feature reduction, the Naive Bayes classifier for the gait analysis, and the Mixture of Gaussian classifiers for the voice analysis. The system achieves high accuracy in filtering non-subject data, more specifically , 84% accuracy on the gait channel and 98% accuracy on the voice signal. These results allow us to generate sufficiently accurate data streams for health monitoring purposes.