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

By Hadi Banaee, Mobyen Uddin Ahmed, Amy Loutfi. Italian National Conference on Sensors, 2013

434 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1b8ac910c710b035c4601dc628587dafec25dad7

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Computer Vision and Pattern Recognition

Citations in the corpus

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes (2019, background): “Machine learning–based data mining tasks could be categorized as descriptive or unsupervised (ie, clustering, association, and summarization), semisupervised (ie, classification and detection), and predictive or supervised learning (ie, classification and regression) [11].”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “future health events, early detection of diseases, cost reduction, improved accuracy in clinical diagnosis, and clinical decision support [173], [174].”
The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review (2023, background): “Knowing that wearable ECG monitors could monitor respiration rate, blood pressure, temperature, and activity levels, AI and ML algorithms could help continuously monitor cardiovascular vital signs to enable early intervention [8,238].”
Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies (2018, background): “Alarm is a subtask mainly used in anomalies detection, raising an alarm as soon as an anomaly is detected [11]; (2.”
Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes (2018, methodology): “[11] reviewed the current methods and algorithms used to analyze data from wearable sensors in healthcare services.”