Article(id=1281608161803080314, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.03.0432, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763049600000, receivedDateStr=2025-11-14, revisedDate=1768406400000, revisedDateStr=2026-01-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488796901, onlineDateStr=2026-07-08, pubDate=1774627200000, pubDateStr=2026-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488796901, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488796901, creator=13701087609, updateTime=1783488796901, updator=13701087609, issue=Issue{id=1281608117335068953, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='3', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1774627200000', pubDateStr='2026-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488786300, creator='13701087609', updateTime=1783588670896, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027063758533259, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027063758533260, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=229, endPage=237, ext={EN=ArticleExt(id=1281608161991823995, articleId=1281608161803080314, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Evaluation of employee's psychological stress status using LSTM with attention mechanism, columnId=1149735805633081985, journalTitle=China Safety Science Journal, columnName=Occupational Health, runingTitle=null, highlight=null, articleAbstract=

To safeguard employees' psychological health and improve the accuracy and interpretability of psychological stress evaluation methods, taking multimodal physiological time-series data as the research object, a LSTM with AM(LSTMA) method was proposed to accurately evaluate employees' psychological stress states in the paper. Firstly, using the multimodal physiological time-series data (Blood Volume Pulse (BVP), Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG), Respiration (RESP), Body Temperature (TEMP), and three-axis Acceleration (ACC)) from the WESAD dataset were adopted as the research carrier, the gating memory mechanism of the modal-specific LSTM modules was utilized to accurately capture cross-time-step temporal dependency features, effectively retain key physiological features strongly associated with psychological states, and filter out short-term random noise, thereby ensuring that the physiological feature data could truly characterize the dynamic evolution of employees' psychological states. Secondly, after feature fusion, the attention mechanism was introduced to adaptively assign attention weight coefficients based on the feature importance of physiological data across different modalities and time steps, enhancing key features and micro-response features sensitive to psychological stress states while suppressing the interference of redundant information. Finally, the accurate evaluation of psychological stress states was accomplished through a fully connected neural network. Experimental results show that the LSTMA method achieves an evaluation accuracy of 94.56% for the four-classification task (neutral, stress, pleasure, and meditation) of psychological stress states. After Leave-One-Out Cross-Validation (LOOCV), the accuracy is improved to 98.08%. Ablation experiments verify the synergistic enhancement effect of the modal-specific LSTM and the attention mechanism, and model interpretability analysis further confirms the scientificity and rationality of LSTMA.

, authors=Haiqing CAO1, Zhiying YAO**, 2, Shuran LYU1, Cuiyou YAO1, authorsList=Haiqing CAO, Zhiying YAO, Shuran LYU, Cuiyou YAO, authorCompany=null, correspAuthors=Zhiying YAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608162713244286, articleId=1281608161803080314, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=融合注意力机制的LSTM职工心理压力状态评价方法, columnId=1281608104106242978, journalTitle=中国安全科学学报, columnName=职业健康, runingTitle=null, highlight=null, articleAbstract=

为确保职工身心健康,提升心理压力评价方法的准确性与可解释性,以多模态生理时间序列数据为研究对象,提出一种融合注意力机制(AM)的长短期记忆(LSTM)网络方法(LSTMA),实现职工心理压力状态的准确评价。首先,以数据集WESAD中多模态生理时间序列数据(血容量脉搏(BVP)、心电图(ECG)、皮肤电活动(EDA)、肌电图(EMG)、呼吸(RESP)、体温(TEMP)和三轴加速度(ACC))为研究对象,通过分模态LSTM模块的门控记忆机制,精准捕获跨时间步时序依赖特征,有效保留与心理状态强相关的关键生理特征,并过滤短期随机噪声,确保生理特征数据能真实表征职工心理状态的动态演化;然后,在特征融合后引入AM,基于各模态、各时间步生理数据的特征重要性自适应分配注意力权重系数,强化对心理压力状态敏感的关键特征与微小响应特征,同时抑制冗余信息干扰;最后,通过全连接神经网络完成心理压力状态准确评价。结果表明:LSTMA在中性、压力、愉悦、冥想4分类任务中,心理压力状态评价准确率达94.56%;经留一法交叉验证后,准确率提升至98.08%;消融试验验证了分模态LSTM与AM的协同增强效应,模型解释性分析进一步佐证LSTMA方法设计的科学性与合理性。

, authors=曹海青副教授1, 姚志英副教授**, 2, 吕淑然教授1, 姚翠友教授1, authorsList=曹海青副教授, 姚志英副教授, 吕淑然教授, 姚翠友教授, authorCompany=null, correspAuthors=姚志英副教授, authorNote=

曹海青(1976—),男,山西吕梁人,博士,副教授,主要从事公共安全、数据化决策和智能算法方面的研究。E-mail:

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** 姚志英(1977—),女,江苏淮安人,博士,副教授,主要从事应急物资管理、公共安全和供应链安全等方面的研究。E-mail:
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曹海青(1976—),男,山西吕梁人,博士,副教授,主要从事公共安全、数据化决策和智能算法方面的研究。E-mail:

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曹海青(1976—),男,山西吕梁人,博士,副教授,主要从事公共安全、数据化决策和智能算法方面的研究。E-mail:

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融合注意力机制的LSTM职工心理压力状态评价方法
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曹海青副教授 1 , 姚志英副教授 **, 2 , 吕淑然教授 1 , 姚翠友教授 1
中国安全科学学报 | 职业健康 2026,36(3): 229-237
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中国安全科学学报 |职业健康 2026 , 36 (3) : 229 -237
融合注意力机制的LSTM职工心理压力状态评价方法
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曹海青副教授1 , 姚志英副教授**, 2 , 吕淑然教授1, 姚翠友教授1
作者信息
  • 1首都经济贸易大学 管理工程学院,北京 100071
  • 2北京物资学院 智能工程与供应链创新学院,北京 101149
通讯作者:
** 姚志英(1977—),女,江苏淮安人,博士,副教授,主要从事应急物资管理、公共安全和供应链安全等方面的研究。E-mail:
作者简介:

曹海青(1976—),男,山西吕梁人,博士,副教授,主要从事公共安全、数据化决策和智能算法方面的研究。E-mail:

Evaluation of employee's psychological stress status using LSTM with attention mechanism
Haiqing CAO1 , Zhiying YAO**, 2 , Shuran LYU1, Cuiyou YAO1
Affiliations
  • 1School of Management and Engineering, Capital University of Economics and Business, Beijing 100071, China
  • 2School of Intelligent Engineering and Supply Chain Innovation, Beijing Wuzi University, Beijing 101149, China
出版时间: 2026-03-28 doi: 10.16265/j.cnki.issn1003-3033.2026.03.0432
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为确保职工身心健康,提升心理压力评价方法的准确性与可解释性,以多模态生理时间序列数据为研究对象,提出一种融合注意力机制(AM)的长短期记忆(LSTM)网络方法(LSTMA),实现职工心理压力状态的准确评价。首先,以数据集WESAD中多模态生理时间序列数据(血容量脉搏(BVP)、心电图(ECG)、皮肤电活动(EDA)、肌电图(EMG)、呼吸(RESP)、体温(TEMP)和三轴加速度(ACC))为研究对象,通过分模态LSTM模块的门控记忆机制,精准捕获跨时间步时序依赖特征,有效保留与心理状态强相关的关键生理特征,并过滤短期随机噪声,确保生理特征数据能真实表征职工心理状态的动态演化;然后,在特征融合后引入AM,基于各模态、各时间步生理数据的特征重要性自适应分配注意力权重系数,强化对心理压力状态敏感的关键特征与微小响应特征,同时抑制冗余信息干扰;最后,通过全连接神经网络完成心理压力状态准确评价。结果表明:LSTMA在中性、压力、愉悦、冥想4分类任务中,心理压力状态评价准确率达94.56%;经留一法交叉验证后,准确率提升至98.08%;消融试验验证了分模态LSTM与AM的协同增强效应,模型解释性分析进一步佐证LSTMA方法设计的科学性与合理性。

注意力机制(AM)  /  长短时记忆(LSTM)网络  /  心理压力  /  状态评价  /  生理时间序列数据  /  多模态

To safeguard employees' psychological health and improve the accuracy and interpretability of psychological stress evaluation methods, taking multimodal physiological time-series data as the research object, a LSTM with AM(LSTMA) method was proposed to accurately evaluate employees' psychological stress states in the paper. Firstly, using the multimodal physiological time-series data (Blood Volume Pulse (BVP), Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG), Respiration (RESP), Body Temperature (TEMP), and three-axis Acceleration (ACC)) from the WESAD dataset were adopted as the research carrier, the gating memory mechanism of the modal-specific LSTM modules was utilized to accurately capture cross-time-step temporal dependency features, effectively retain key physiological features strongly associated with psychological states, and filter out short-term random noise, thereby ensuring that the physiological feature data could truly characterize the dynamic evolution of employees' psychological states. Secondly, after feature fusion, the attention mechanism was introduced to adaptively assign attention weight coefficients based on the feature importance of physiological data across different modalities and time steps, enhancing key features and micro-response features sensitive to psychological stress states while suppressing the interference of redundant information. Finally, the accurate evaluation of psychological stress states was accomplished through a fully connected neural network. Experimental results show that the LSTMA method achieves an evaluation accuracy of 94.56% for the four-classification task (neutral, stress, pleasure, and meditation) of psychological stress states. After Leave-One-Out Cross-Validation (LOOCV), the accuracy is improved to 98.08%. Ablation experiments verify the synergistic enhancement effect of the modal-specific LSTM and the attention mechanism, and model interpretability analysis further confirms the scientificity and rationality of LSTMA.

attention mechanism (AM)  /  long short-term memory (LSTM) network  /  psychological stress  /  status evaluation  /  physiological time-series data  /  multimodal
曹海青副教授, 姚志英副教授, 吕淑然教授, 姚翠友教授. 融合注意力机制的LSTM职工心理压力状态评价方法. 中国安全科学学报, 2026 , 36 (3) : 229 -237 . DOI: 10.16265/j.cnki.issn1003-3033.2026.03.0432
Haiqing CAO, Zhiying YAO, Shuran LYU, Cuiyou YAO. Evaluation of employee's psychological stress status using LSTM with attention mechanism[J]. China Safety Science Journal, 2026 , 36 (3) : 229 -237 . DOI: 10.16265/j.cnki.issn1003-3033.2026.03.0432

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doi: 10.16265/j.cnki.issn1003-3033.2026.03.0432
  • 接收时间:2025-11-14
  • 首发时间:2026-07-08
  • 出版时间:2026-03-28
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  • 收稿日期:2025-11-14
  • 修回日期:2026-01-15
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    1首都经济贸易大学 管理工程学院,北京 100071
    2北京物资学院 智能工程与供应链创新学院,北京 101149

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** 姚志英(1977—),女,江苏淮安人,博士,副教授,主要从事应急物资管理、公共安全和供应链安全等方面的研究。E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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