Name
Abstract | ||
|---|---|---|
Mental fatigue is a kind of mental exhaustion phenomenon caused by heavy mental work, excessive nervous tension or monotonous work for a long time, which brings great harm to traffic, construction and other fields. Hence, more and more attention has been paid to the research of fatigue prediction methods in recent years. The commonly used fatigue prediction methods fail to accurately predict the fatigue state without adding the influence of physiological factors on fatigue. In this paper, we introduce a fatigue prediction model based on subjective alertness model and physiological parameters, which can be used to predict the fatigue state of human body through deep sleep time, sleep time and workload (physiological parameters) in the future. Specifically, the input is the physiological parameters of the previous day, and the output is fatigue state of the following day. In order to avoid individual differences, this model is modified and verified by multimodal real-time monitoring and self-learning methods. Experiments show that our model is effective, accurate and convenient in practice, which is beneficial to fatigue prediction. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2941043 | IEEE ACCESS |
Keywords | DocType | Volume |
Mental fatigue, prediction model, physiological parameters, subjective alertness, multimodal | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anping Song | 1 | 0 | 0.34 |
| Chaoqun Niu | 2 | 0 | 0.34 |
| Xuehai Ding | 3 | 0 | 0.34 |
| Xiaokang Xu | 4 | 0 | 0.34 |
| Ziheng Song | 5 | 1 | 0.69 |