Emergent trend detection in diurnal activity.
When monitoring elders' daily routines, it is desirable to track significant deviations from a baseline pattern, as consecutive, aberrant days may foreshadow a need for medical attention. However, many traditional, unsupervised methods for pattern classification are ill-suited for this task, as they are incapable for adapting to additive datasets. To surmount this shortcoming, we establish a framework for recognizing temporal trends in feature data extracted from passive sensors.
Sledge
Isaac J
IJ
Electrical and Computer Engineering Department, University of Missouri, Columbia, MO 65211, USA. ijs3h6@missouri.edu
Keller
James M
JM
Alexander
Gregory L
GL
D000203:Activities of Daily Living; D000465:Algorithms; D001185:Artificial Intelligence; D002940:Circadian Rhythm; D016000:Cluster Analysis; D004867:Equipment Design; D006299:Health Services for the Aged; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D009474:Neurons / Q000473:pathology; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software