Measuring Regularity in Daily Behavior for the Purpose of Detecting Alzheimer

This paper presents a study of sensor data from a person who developed Alzheimer's disease during a 4-year monitoring period and who is monitored with simple ambient sensors in her home. Our aim is to find data analysis methods that reveal relevant changes in the sensor pattern that occur before the diagnosis. We focus on the quantification of regularity, which is identified as a relevant indicator for the assessment of a disease such as Alzheimer's. Two unsupervised methods are studied. Restricted Boltzmann Machines are trained and the resulting weights are visualized to see whether there are changes in regularity in the behavioral pattern. Fast Fourier Transformation is applied to the sensor data and the spectral characteristics are determined and compared with the same purpose. Both methods reveal changes in the pattern between different periods. Both methods therefore are useful in quantifying and understanding changes in the regularity of the daily pattern.