基于 XGBoost 算法的生理数据识别攀爬工人疲劳
Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm.
机构信息
Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China.
State Grid Shandong Electric Power Company, Jinan, China.
出版信息
Front Public Health. 2024 Oct 9;12:1462675. doi: 10.3389/fpubh.2024.1462675. eCollection 2024.
BACKGROUND
High-voltage workers often experience fatigue due to the physically demanding nature of climbing in dynamic and complex environments, which negatively impacts their motor and mental abilities. Effective monitoring is necessary to ensure safety.
METHODS
This study proposed an experimental method to quantify fatigue in climbing operations. We collected subjective fatigue (using the RPE scale) and objective fatigue data, including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood oxygen saturation (SpO), vital capacity (VC), grip strength (GS), response time (RT), critical fusion frequency (CFF), and heart rate (HR) from 33 high-voltage workers before and after climbing tasks. The XGBoost algorithm was applied to establish a fatigue identification model.
RESULTS
The analysis showed that the physiological indicators of SpO, VC, GS, RT, and CFF can effectively evaluate fatigue in climbing operations. The XGBoost fatigue identification model, based on subjective fatigue and the five physiological indicators, achieved an average accuracy of 89.75%.
CONCLUSION
This study provides a basis for personalized management of fatigue in climbing operations, enabling timely detection of their fatigue states and implementation of corresponding measures to minimize the likelihood of accidents.
背景
高压作业人员在动态复杂环境中攀爬时,身体负荷较大,容易产生疲劳,这会对其运动和精神能力产生负面影响。因此,需要对其进行有效的监测以确保安全。
方法
本研究提出了一种量化攀爬作业中疲劳的实验方法。我们采集了 33 名高压作业人员攀爬前后的主观疲劳(使用 RPE 量表)和客观疲劳数据,包括收缩压(SBP)、舒张压(DBP)、血氧饱和度(SpO)、肺活量(VC)、握力(GS)、反应时间(RT)、临界融合频率(CFF)和心率(HR)。采用 XGBoost 算法建立了疲劳识别模型。
结果
分析表明,SpO、VC、GS、RT 和 CFF 等生理指标可有效评估攀爬作业中的疲劳情况。基于主观疲劳和五个生理指标的 XGBoost 疲劳识别模型的平均准确率为 89.75%。
结论
本研究为攀爬作业中疲劳的个性化管理提供了依据,可以及时检测到他们的疲劳状态,并采取相应措施将事故发生的可能性降到最低。