Bed-time sensors - characterization and comparison

Abstract: The population of the world is aging. In Sweden alone, almost 20% of the population is 65 years or older. As people get older, problems with sleep disturbances and sleep quality tends to increase, as do the risks of falling injuries. In this thesis, methods for calculating sleep quality and if a person is about to leave a bed were devised. A bed sensor, measuring ballistocardiographical signals, was used to measure activity in bed and vital signs of the occupant. The Cole-Kripke algorithm, used to calculate sleep quality based on activity from a wrist worn sensor, was adapted to the bed sensor system and compared to results from the ActiGraph wGT3X-BT activity monitor, which is frequently used in research. The bed sensor systems sleep quality estimations showed strong correlation with the ActiGraph, with a Pearson correlation coefficient of 0.946. Two approaches were made to estimate if a subject was about to leave the bed, one by training a neural network on labeled night data, and one using a linear equation with each term consisting of activity data, optimized by linear regression. The neural network approach suffered from limited data, but the linear method showed more promise, with accuracy, specificity and sensitivity all over 70%.