Name
Title | ||
|---|---|---|
A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care. |
Abstract | ||
|---|---|---|
•We proposed an activity state representation for arbitrary sensor combinations.•We developed a Seq2Seq model-based activity recognition framework.•The framework provides an end-to-end recognition from raw data to activities.•Our method out-performed benchmark methods on two publicly available datasets.•The model shows potential for real-world smart home monitoring. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jbi.2018.07.006 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Activity of daily living,ADL recognition,Deep learning,Activity state representation,Sequence-to-sequence model | State representation,Activities of daily living,Information retrieval,Monitoring system,Computer science,Baseline (configuration management),Home automation,Feature engineering,Human–computer interaction,Artificial intelligence,Deep learning | Journal |
Volume | ISSN | Citations |
84 | 1532-0464 | 0 |
PageRank | References | Authors |
0.34 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyi Zhu | 1 | 3 | 5.21 |
| Hsinchun Chen | 2 | 9569 | 813.33 |
| Randall A Brown | 3 | 24 | 2.65 |