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Sensors 2013, 13(12), 17472-17500; doi:10.3390/s131217472
Review

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

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Received: 20 September 2013; in revised form: 15 November 2013 / Accepted: 6 December 2013 / Published: 17 December 2013
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract: The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.
Keywords: data mining; wearable sensors; healthcare; physiological sensors; health monitoring system; machine learning technique; vital signs; medical informatics data mining; wearable sensors; healthcare; physiological sensors; health monitoring system; machine learning technique; vital signs; medical informatics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Banaee, H.; Ahmed, M.U.; Loutfi, A. Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges. Sensors 2013, 13, 17472-17500.

AMA Style

Banaee H, Ahmed MU, Loutfi A. Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges. Sensors. 2013; 13(12):17472-17500.

Chicago/Turabian Style

Banaee, Hadi; Ahmed, Mobyen U.; Loutfi, Amy. 2013. "Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges." Sensors 13, no. 12: 17472-17500.


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