Fang, X; Li, H; Ma, J; Xing, X; Ren, Q; Umer, W and Wang, L (2024) Online assessment of spontaneous mental fatigue in construction workers considering data quality: Improved online sequential extreme learning machine. Journal of Construction Engineering and Management, 150(11): 04024148, ISSN 0733-9364
Abstract
Biological data-based methods for monitoring workers' mental fatigue have become widely adopted in recent years. However, few have concentrated on the online monitoring and assessment of mental fatigue considering the complexity and high dimension of the biological data, especially for scenarios where data arrives continuously in the form of flows. This study aimed to propose an online learning model to learn model parameters according to the order of data acquisition. Specifically, the fuzziness-based online sequential extreme learning machine (Fuzziness-OS-ELM) model was proposed, consisting of two parts: (1) a data value estimator; and (2) an online mental fatigue classification model. As new data arrives, the Fuzziness-OS-ELM model can effectively identify and select samples with high data quality based on fuzziness, which are then used to continuously update the online mental fatigue classification model. A cognitive experiment was carried out to evaluate the Fuzziness-OS-ELM model. The results indicated that samples with low fuzziness corresponded to high data quality. The proposed online sequential learning model exhibited enhanced classification performance on mental fatigue. This study's dynamic diagnostic method for identifying the onset and progression of mental fatigue can provide targeted support for precise interventions aimed at construction workers.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | data quality assessment; electroencephalography (EEG) and electrocardiogram; fuzziness analysis; mental fatigue monitoring; online sequential learning |
| Index terms: | complexity, quality assessment, monitoring, estimator, fatigue, dimension, data acquisition, construction worker, experiment |
| Subjects: | quality assurance, profession, practitioner, systems engineering, control systems, data collection methods, health conditions and diseases, health monitoring assessment and metrics |
| Topics: | Health and Safety, Quality Management, Engineering Principles, Site Management, Roles and Professions, Research Practice |
| Descriptive scope: | 3 PCE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here