Principles of data mining
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
A Weighted Moving Average-based Approach for Cleaning Sensor Data
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Preprocessing in a tiered sensor network for habitat monitoring
EURASIP Journal on Applied Signal Processing
Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals
Computer Methods and Programs in Biomedicine
A neuro-fuzzy approach for sensor network data cleaning
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
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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.