Paper Info
Title
Measuring Routine Variability of Daily Activities with Image Complexity Metrics.
Abstract
Since the abrupt change of daily routines can be an early symptom of cognitive impairment, it is important to measure and track the variability of daily routines of the elderly living alone in terms of their healthcare. This study is motivated by the idea that the degree of image complexity manifested in a person's day-to-day schedule chart is related to the degree of routine variability of his/her daily activities. To test this idea, synthetic data on daily activity logs containing varying degrees of routine variability was created, and a schedule bar chart image was generated based on the synthetic data. Then this study examines whether and to what extent the routine variability inherent in the dataset can be measured by existing image complexity metrics, which have been used to a pattern tendency of an image. The results indicate that the outcomes from three metrics, including Shannon Entropy, GLCM-Entropy, and GLCM-Energy, are well-associated with the degree of routine variability manifested in different aspects of daily activity schedules (i.e., start-time, duration, non-routine-contributing activities, and sequence of routine-contributing activities).
Year
DOI
Venue
2019
10.1145/3360322.3361009
BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation New York NY USA November, 2019
Keywords
Field
DocType
Routine variability,Image complexity,Smart-home healthcare
Activities of daily living,Computer science,Control engineering,Image complexity,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-7005-9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Bogyeong Lee101.35
Changbum Ryan Ahn202.03
Prakhar Mohan301.35
Theodora Chaspari43819.43
Hyunsoo Lee53711.75