In this paper, we propose a method for activity recognition, which can estimate new activities which does not appear in the training data, combining  word vectors  constructed from semantic word vectors and  Twitter timestamps.  Because traditional activity recognition utilizes supervised machine learning, unknown activity classes which do not appear in the training dataset is unable to be estimated. For this problem, zero-shot machine learning method is proposed, but it requires the preparation of semantic codes. As semantic codes, we utilize word vectors constructed from semantic word vectors and Twitter timestamps. To evaluate the proposed method, we evaluated whether we could estimate unknown activity classes, with the sensor data set collected from 20 households for 4 months, along with the user-generated labels using the web system which can estimate, modify, and add new activity types. As a result, the proposed method could even estimate unknown activity classes. Moreover, by utilizing Twitter timestamps and semantic word vectors from the Japanese Wikipedia in word vectors, the method could estimate 9 unknown activity classes.
['Moe Matsuki', 'Sozo Inoue']
Recognizing unknown activities using semantic word vectors and twitter timestamps