Recognizing Unknown Activities Using Semantic Word Vectors and Twitter Timestamps

Reviewed
Moe Matsuki, Sozo Inoue,
Ubicomp Workshop for Human Activity Sensing Corpus and its Application (HASCA)
(Not Available)
(Not Available)
823-830
2016-09-12
Heidelberg, Germany
http://hasca2016.hasc.jp
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. Be- cause 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 pro- posed, 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 re- sult, 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.

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