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

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 c...

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Bibliographic Details
Main Authors: Banaee, Hadi, Ahmed, Mobyen Uddin, Loutfi, Amy
Format: Conference Proceeding
Language:English
Subjects:
Biomedical monitoring
body area networks
Computer Science
Data analysis
Datavetenskap
Health monitoring
Heart rate
linguistic summarisation
natural language generation
Natural languages
physiological data analysis
Pragmatics
Time series analysis
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Summary: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.
ISSN:1062-922X
2577-1655
DOI:10.1109/SMC.2013.661