Novel biometrics: Analysis of an unattended health monitoring system

In-home monitoring technologies have the potential to transform the healthcare system. With the increasing number and variety of connected devices that can be accessed by multiple users, the need for seamless authentication and identification mechanisms is greater now than ever. Seamless, non-interruptive mechanisms are required to personalize these devices and non-intrusive biometric technologies are gaining popularity as an answer to this automated personalization challenge. In this work, we evaluate the biometric efficacies of a fully integrated toilet (FIT) seat, designed for monitoring a subjects cardiac health parameters such as electrocardiogram (ECG), ballistocardiogram (BCG) and weight measures, all in the home without any change in daily habits. We assess the system on data obtained from 22 healthy subjects with measurements taken over an 8-week period, in order to obtain the optimal combination of features, classifiers and enrollment record size. We found the multi-class SVM classifier along with all features extracted from the raw measurements to be the best performing combination. This combination produced an area under curve (AUC) value of 0.86. When tested for its person identification performance, we obtained an average accuracy of 75%. We have therefore demonstrated that the biometric capacities of the FIT seat shows a strong potential for deployment in real-life, multi-user, in-home settings.