Name
Title | ||
|---|---|---|
Modeling long-term human activeness using recurrent neural networks for biometric data. |
Abstract | ||
|---|---|---|
This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12911-017-0453-1 | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
Activeness prediction,Calorie,Footstep,Heart rate,Recurrent neural network,Time series modeling | Data mining,Computer science,Recurrent neural network,Data type,Correlation,Artificial intelligence,Simple linear regression,Biometrics,Artificial neural network,Machine learning,Linear regression,Autocorrelation | Journal |
Volume | Issue | Citations |
17 | S-1 | 0 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zae Myung Kim | 1 | 5 | 4.21 |
| Hyungrai Oh | 2 | 1 | 1.02 |
| Han-Gyu Kim | 3 | 0 | 1.01 |
| Chae-Gyun Lim | 4 | 22 | 9.50 |
| KyoJoong Oh | 5 | 25 | 10.03 |
| Ho-Jin Choi | 6 | 280 | 53.61 |