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Recent years have seen an increase in user activity support services. Recognition of detailed user activities is essential to the provision services of high levels of convenience. However, this gives rise to the problem of the need for massive numbers of man-hours required to create feature values such as classifiers and rules necessary for recognition of on-site activities. We are developing primitive behavior grouping technology that can recognize activity partitions without teachers by automatically constructing feature quantities for behavior recognition. This technology presented the problem of degradation of the accuracy of recognition of activity partitions due to the effects of phenomena, such as temporary user movements or noise when applied in an actual environment in which log acquisition frequency was inhibited because of reduced power consumption. This time, we have proposed a multiphase activity recognition technology capable of highly-accurate recognition of activity partitions by defining the period of macro-granular user activities as the activity scene recognized from the state of the user surrounds and compared with activity partition information. This technology makes it possible to correct misrecognized information which was impossible to distinguish by single activity partitions and to realize improved accuracy in activity partition recognition in usage scenes that assumed an actual environment focused on white-collar users likely to exhibit changes in activities. The technology was applied to the activity recognition system focused on white-collar users and, as a result, an improvement in the activity partition recognition rate of 20% or more for our previous developed technology and performance equivalent to the conventional technologies using the most frequent value and the activity flow information were confirmed. We also confirmed that it is possible to realize recognition of activity partitions with high granularity equivalent to the schedule which could not be recognized by the conventional technique with high accuracy.

Multiphase Activity Recognition System using Activity Scene