Name
Title | ||
|---|---|---|
A neural-network-based investigation of eye-related movements for accurate drowsiness estimation. |
Abstract | ||
|---|---|---|
Many studies reported that eye-related movements, e.g., blank stares, blinking and drooping eyelids, are highly indicative symptoms of drowsiness. However, few researchers have investigated the computational efficacy accounted for drowsiness estimation by these eye-related movements. This paper thus analyzes two typical eye-related movements, i.e., eyelid movements Xel(t) and eyeball movements Xeb(t), and investigates neural-network-based approaches to model temporal correlations. Specifically, we compare the effectiveness of three combinations of eye-related movements, i.e., [Xel(t)], [Xeb(t)], and [Xel(t),Xeb(t)], for drowsiness estimation. Furthermore, we investigate the usefulness of two typical types of neural networks, i.e., CNN-Net and CNNLSTM-Net, for better drowsiness modeling. The experimental results show that [Xel(t),Xeb(t)] can achieve a better performance than [Xel(t)] for short time drowsiness estimation while [Xeb(t)]alone performs worse even than the baseline method (PERCLOS). In addition, we found that CNN-Net are more effective for accurate drowsiness level modeling than CNNLSTM-Net. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513491 | EMBC |
Field | DocType | Volume |
Computer vision,Eyeball movements,Computer science,Artificial intelligence,Artificial neural network,Hidden Markov model,Drooping eyelids | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingfei Sun | 1 | 4 | 1.78 |
| Masanori Tsujikawa | 2 | 1 | 3.40 |
| Yoshifumi Onishi | 3 | 0 | 0.68 |
| Xiaojuan Ma | 4 | 325 | 49.27 |
| Atsushi Nishino | 5 | 0 | 1.01 |
| Satoshi Hashimoto | 6 | 9 | 1.68 |