Using Off-Line Handwriting to Predict Blood Pressure Level: A Neural-Network-Based Approach

Abstract

We propose a novel, non-invasive, neural-network based, three-layered architecture for determining blood pressure levels of individuals solely based on their handwriting. We employ four handwriting features (baseline, lowercase letter “f”, connecting strokes, writing pressure) and the result is computed as low, normal or high blood pressure. We create our own database to correlate handwriting with blood pressure levels and we show that it is important to use a predefined text for the handwritten sample used for training the system in order to have high prediction accuracy, while for further tests any random text can be used, keeping the accuracy at similar levels. We obtained over 84% accuracy in intra-subject tests and over 78% accuracy in inter-subject tests. We also show there is a link between several handwriting features and blood pressure level prediction with high accuracy which can be further exploited to improve the accuracy of the proposed approach.