ArticleBioMed research international2014
Heart health risk assessment system: a nonintrusive proposal using ontologies and expert rules.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Content and quality of physical activity ontologies: a systematic review.The international journal of behavioral nutrition and physical activity · 2023Pooled it
- Core reference ontology for individualized exercise prescription.Scientific data · 2024Article
- A Novel Smartphone Accelerometer Application for Low-Intensity Activity and Energy Expenditure Estimations in Overweight and Obese Adults.Journal of medical systems · 2017Article
- Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network.Sensors (Basel, Switzerland) · 2017Article
- Lynx: Automatic Elderly Behavior Prediction in Home Telecare.BioMed research international · 2015Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the World Health Organization, the world's leading cause of death is heart disease, with nearly two million deaths per year. Although some factors are not possible to change, there are some keys that help to prevent heart diseases. One of the most important keys is to keep an active daily life, with moderate exercise. However, deciding what a moderate exercise is or when a slightly abnormal heart rate value is a risk depends on the person and the activity. In this paper we propose a context-aware system that is able to determine the activity the person is performing in an unobtrusive way. Then, we have defined ontology to represent the available knowledge about the person (biometric data, fitness status, medical information, etc.) and her current activity (level of intensity, heart rate recommended for that activity, etc.). With such knowledge, a set of expert rules based on this ontology are involved in a reasoning process to infer levels of alerts or suggestions for the users when the intensity of the activity is detected as dangerous for her health. We show how this approach can be accomplished by using only everyday devices such as a smartphone and a smartwatch.
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Registered trials
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