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|Title: ||A New Habit Pattern Learning Scheme in Smart Home|
|Authors: ||Wang, Pingquan;Luo, Hong;Li, Xinming;Zhao, Zhongwen|
|Keywords: ||Smart Home;Habit Pattern;Time Series;Activity Recognition|
|Issue Date: ||2016-12-20 09:46:51 (UTC+8)|
|Abstract: ||Most of the user’s activities are consistent with their habits, therefore in this paper, we propose
a new habit pattern learning scheme in smart home to better obtain user’s behavior regulations and
habits, which can make the home more intellectually interact with people. By recording the operations
on each electric appliance in the form of time series, we firstly find out that the habit can be classified
into fixed-length habit and timing habit. Then, we propose habit extraction methods based on the
corresponding activity probability and calculation formulas of the habit strength. Since different habits
have different variation characteristics on habit strength and time zone, we further propose the
self-learning algorithms on time zone and habit strength threshold to obtain the suitable parameters.
Furthermore, by defining the association among individual habits into selection, parallel, sequence,
cross and inclusion, we can obtain habit set which is a group of habits with inner correlation. In order to
adapt to the habit variations, we introduce a habit change factor into the habit pattern discovery
algorithm so as to follow the habit changes. Finally, we construct the experimental environment in a
real smart home, analyze and calculate the operation records of electric appliances in two months. The
experiment results show that the proposed habit pattern learning scheme is effective and efficient.
|Relation: ||Journal of Applied Science and Engineering 19(1), pp.83-94|
|Appears in Collections:||[淡江理工學刊] 第19卷第1期|
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