In recent years, location-based technologies for ubiquitous environments have transformed individuals' current location data into valuable assets. To establish advanced indoor location-based services, highly accur...
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ISBN:
(纸本)9783031421402;9783031421419
In recent years, location-based technologies for ubiquitous environments have transformed individuals' current location data into valuable assets. To establish advanced indoor location-based services, highly accurate positioning technology is required to precisely recognize and predict the movements of humans and objects. Hence, we proposed a method for extracting a user's activitypatterns from time-series ultra-wideband (UWB) tag data in indoor environments. The proposed method consists of three steps: 1) estimate the user's stay regions from the user's location history using UWB sensors attached to the user, 2) assign each stay region to significant indoor activities, and 3) mine the activitypatterns and their characteristics of the user from the sequence of indoor activities. In our experiments, we confirmed that the proposed method performed better in recognizing activity regions indoors and that the activitypatterns of each member in the laboratory were discovered in a practical environment using the proposed method.
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