Authors:
Takahiko Shintani
;
Tadashi Ohmori
and
Hideyuki Fujita
Affiliation:
Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo and Japan
Keyword(s):
Daily Life, Activity Data, Comparison Method, Clustering, Healthcare.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Clustering and Classification Methods
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Recently, a large amount of physical activity data has been obtained via wearable sensors collected as lifelogs. The long-term daily lives of users can be understood using such long-term activity data. In this paper, we investigate a method for comparing two distinct periods of the daily life of a user to understand the long-term characteristics of that user’s daily life. Our method uses only activity data that can be collected easily and continuously over the long term using wearable sensors. There are various ways in which humans can spend a day, and a period of daily life consists of a set of several days spent in several manners. We compare two periods of daily life by considering the manner in which a day is spent. The manner in which a day is spent can be distinguished based on the activities that are performed on a given day. The amount of movement differs depending on the activity, and similar amounts of movement are measured when similar activities are performed. We focus on
this point to classify how each day is spent. Further, we distinguish the manner in which a day is spent based on similarities in the time series data with respect to the levels of the activities by noting the main sleeping period, which is an important behavior in daily human life. We propose a method to compare two distinct periods of daily life based on the distribution of the manner in which a day is spent in each period. The effectiveness of the methods proposed in this paper is evaluated via experimental evaluations using real datasets.
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