Measuring Regularity in Daily Behavior for the Purpose of Detecting Alzheimer

Abstract

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.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

“…Body motion behavior has been widely studied in detecting AD based on a range of sensors ( Robben et al, 2016 ). Those utilized biosensors are mainly wearable sensors, including feet and waist-mounted inertial sensors, ankle-mounted accelerometric sensors, wrist-mounted accelerometers, leg-mounted force sensors, etc.…”

Οι Ψηφιακοί Βιοδείκτες Ως Οικολογικά Έγκυρα Μέσα Απομακρυσμένης Και Μακρόχρονης Εκτίμησης Της Υγείας Των Ατόμων Τρίτης Ηλικίας

“…Επιπλέον, η νόσος Αλτσχάιμερ φάνηκε να έχει επιπτώσεις πάνω στα επίπεδα του στρες, στην διάθεση και τον ύπνο. Οι επιπτώσεις που εμφανίζει η νόσος σε νοητικούς τομείς, και λειτουργικές διαδικασίες μπορούν να καταγραφούν με ακρίβεια με νέες τεχνολογικές μεθόδους, όπως είναι φορετές συσκευές (wearables), κινητές συσκευές και τεχνολογίες μέτρησης σε περιβάλλοντες χώρους, όπως οικίες [13], [18]- [31].…”