IJERPH, Vol. 16, Pages 3955: Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning

IJERPH, Vol. 16, Pages 3955: Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning International Journal of Environmental Research and Public Health doi: 10.3390/ijerph16203955 Authors: Junfang Gong Runjia Li Hong Yao Xiaojun Kang Shengwen Li 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...
Source: International Journal of Environmental Research and Public Health - Category: Environmental Health Authors: Tags: Article Source Type: research