Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning

Junfang Gong, Runjia Li, Hong Yao, Xiaojun Kang and Shengwen Li
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Junfang Gong: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Runjia Li: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Hong Yao: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Xiaojun Kang: School of Computer Science, China University of Geosciences, Wuhan 430074, China
Shengwen Li: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

IJERPH, 2019, vol. 16, issue 20, 1-15

Abstract: The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there are still some challenges to accurately recognizing posts because existing studies ignore contextual information or word order in posts and remain unsatisfactory for capturing the activity semantics of words. To address this problem, we propose a general model for recognizing the human activity category based on deep learning. This model not only describes how to extract a sequence of higher-level word phrase representations in posts based on the deep learning sequence model but also how to integrate temporal information and external knowledge to capture the activity semantics in posts. Considering that no benchmark dataset is available in such studies, we built a dataset that was used for training and evaluating the model. The experimental results show that the proposed model significantly improves the accuracy of recognizing the human activity category compared with traditional classification methods.

Keywords: human activity category recognition; social media; deep learning; long short-term memory network (LSTM); temporal information encoding (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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