Data accuracy aware mobile healthcare applications

This paper proposes a new approach for online detection and isolation of inaccurate vital sign measurements in mobile healthcare applications. Our primary objective is to distinguish between inaccurate measurement and patient health degradation to reduce false medical alarms. The proposed approach couples dimensionality reduction with inaccurate data detection and isolation. On the one hand, dimension reduction is based on robust incremental Principal Component Analysis. On the other hand, multivariate anomaly detection relies on squared prediction errors and anomalous vital sign isolation is based on contribution plots. We apply our proposed approach on real medical dataset. Our simulation results prove the effectiveness of our approach in achieving good recall with a low false alarm rate compared with existing solutions. The benefit gained by our approach in terms of time and space complexities make it useful and efficient for real time mobile healthcare applications.