A Survey on Deep Learning for Human Activity Recognition
| العنوان: | A Survey on Deep Learning for Human Activity Recognition |
|---|---|
| المؤلفون: | Shahrokh Valaee, Baoding Zhou, Fuqiang Gu, Mark Chignell, Xue Liu, Mu-Huan Chung |
| المصدر: | ACM Computing Surveys. 54:1-34 |
| بيانات النشر: | Association for Computing Machinery (ACM), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | General Computer Science, Cover (telecommunications), business.industry, Computer science, Deep learning, Data science, Theoretical Computer Science, Activity recognition, Home automation, Taxonomy (general), Key (cryptography), Artificial intelligence, Mobile sensing, business |
| الوصف: | Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods. |
| تدمد: | 1557-7341 0360-0300 |
| DOI: | 10.1145/3472290 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::10afddb12ff108d4cda1af9075106c20 https://doi.org/10.1145/3472290 |
| رقم الانضمام: | edsair.doi...........10afddb12ff108d4cda1af9075106c20 |
| قاعدة البيانات: | OpenAIRE |
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