An introduction to variable and feature selection
The Journal of Machine Learning Research
On FastMap and the Convex Hull of Multivariate Data: Toward Fast and Robust Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of movement in bed using unobtrusive load cell sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Multi-modal non-intrusive sleep pattern recognition in elder assistive environment
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
Passive sleep actigraphy: evaluating a non-contact method of monitoring sleep
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
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Based upon current sleep actigraphy techniques, this paper discusses an alternative non-contact method of sleep profiling that is potentially more suitable for long term monitoring than current clinically approved techniques. The passive sleep actigraphy PSA platform presented here utilises strategically positioned accelerometers fixed on a mattress to quantify the recorded movements of a bed occupant. In this work, data captured from a young control group is decomposed into gravitational and inertial components. These components are then translated into activity counts using numerous quantification modalities and feature extraction techniques to isolate the most discriminant attributes for optimal sleep/wake classification. These attributes were then input into a random forest classifier to determine the sleep/wake state of each subject based on their recoded actigraphy data with an accuracy of 89%. The findings suggest that the PSA platform is a potentially viable method of non-contact sleep profiling hence supporting further research into this approach.