Time-series modeling of long-term weight self-monitoring data

被引:0
作者
Helander, Elina [1 ]
Pavel, Misha [2 ,3 ]
Jimison, Holly [2 ,3 ]
Korhonen, Ilkka [1 ]
机构
[1] Tampere Univ Technol, Dept Signal Proc, Personal Hlth Informat Grp, FIN-33101 Tampere, Finland
[2] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA
[3] Northeastern Univ, Bouve Coll Hlth Sci, Boston, MA 02115 USA
来源
2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2015年
关键词
PATTERNS;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.
引用
收藏
页码:1616 / 1620
页数:5
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