Utilising wireless sensor networks towards establishing a method of sleep profiling

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.