Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges

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.

Figure 1.

A schematic overview of the position of the main data mining tasks (anomaly detection, prediction, and diagnosis/decision making) in relation to the different aspects of wearable sensing in the health monitoring systems.

Figure 2.

The outline of the distribution of three data mining tasks in relation to the vital signs measured by wearable sensors considering in this review.

Figure 3.

A generic architecture of the main data mining approach for wearable sensor data.

Figure 4.

The outline of the application of data mining methods in relation to the vital signs measuring with wearable sensors.

Figure 5.

The distribution of works on two sensor data properties (time horizon and scale), with the relation to three types of data acquisition.

Figure 6.

The distribution of works on three sensor data properties (continuous/discrete, labeling, single sensor/multi sensors), with the relation to three types of data acquisition.