Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning
Bibliographic Data
| ID | 15512665 |
|---|---|
| Authors | Junfang Gong (0000-0001-6176-9671, China University of Geosciences), Runjia Li (China University of Geosciences), Hong Yao (0000-0002-7161-8639, China University of Geosciences), Xiaojun Kang (0000-0002-5628-0545, China University of Geosciences), Shengwen Li (0000-0002-1829-4006, China University of Geosciences, corresponding author) |
| Year | 2019 |
| Volume | 16 |
| Issue | 20 |
| Pages | 3955-3955 |
| Publication date | 2019-10-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16203955 |
| PMID | 31627356 |
| OpenAlex | W2981257919 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
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
Activity recognition · Benchmark (surveying · Data science · Deep learning · Machine learning · Natural language processing · Phrase · Semantics (computer science · Sequence (biology · Social media · Word (group theory · World Wide Web · Complex Network Analysis Techniques · Computer Science · Context-Aware Activity Recognition Systems · Human Mobility and Location-Based Analysis · Artificial Intelligence
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |