Robust preprocessing for health care monitoring framework
Abstract
Remote health care monitoring is an emerging application that helps to reduce the cost of health care and at the same time improve its quality. However, by its nature, medical sensor data is often unreliable and massive as the data is collected from numerous sensors operating in noisy environments. Therefore, ensuring the reliability of the sensor data and the scalability of the health care monitoring services are among major challenge and is a determining factor in the success of the system. In this paper, we propose a robust and flexible preprocessing module as part of an active health care monitoring framework. It is responsible for preparing the sensor data and performing some initial assessment of the data for input to later modules. The proposed preprocessing architecture contains five stages: validation, transformation, cleaning, reduction and cross-verification. The module is evaluated using synthetic blood pressure data.
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Several data mining techniques are applied for these tasks: wavelet analysis for artifact reduction and data compression, rule-based methods for data summarizing and transmitting, and Gaussian process for secure authentication.
“In order to conduct these problems, several data mining techniques are applied such as wavelet analysis for artifact reduction [53] and data compression [54], rule-based methods for data summarizing and transmitting [55,56], and Gaussian process for secure authentication [57].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [56]
Some data analysis systems in healthcare were designed to process short signals such as few minutes of ECG data, a few hours of heart rate or oxygen saturation, and measurement of blood pressures for a day.
“Some data analysis systems in healthcare were designed to process short signals such as few minutes of ECG data [74,92], a few hours of heart rate or oxygen saturation [60,61] and the measurement of blood pressures for a day [56].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [56]
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- Authors
- Ahmad, N. F., Hoang, D. B., & Phung, M. H.
- Affiliations
- University of Technology Sydney (AU)
- ORCID
- Hoang, Doan B.
- Venue
- 2009 11th International Conference on e-Health Networking, Applications and Services (Healthcom)
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 169-174
- OpenAlex
- W2144263768
- Cited by
- 21
- References
- 20
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3 · 2021: 1 · 2020: 1 · 2018: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 3
- FWCI
- 1.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) (publishedVersion)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- medical sensor data, data preprocessing, sensor data reliability, data cleaning, data validation, data transformation, data reduction, cross-checking, noisy sensor data
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