A Framework for Automatic Text Generation of Trends in Physiological Time Series Data

Abstract

Health monitoring systems using wearable sensors have rapidly grown in the biomedical community. The main challenges in physiological data monitoring are to analyse large volumes of health measurements and to represent the acquired information. Natural language generation is an effective method to create summaries for both clinicians and patients as it can describe useful information extracted from sensor data in textual format. This paper presents a framework of a natural language generation system that provides a text-based representation of the extracted numeric information from physiological sensor signals. More specifically, a new partial trend detection algorithm is introduced to capture the particular changes and events of health parameters. The extracted information is then represented considering linguistic characterisation of numeric features. Experimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.