一种面向情绪压力分布外检测的多任务跨模态学习方法

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中图分类号：TP391 文献标志码：A 文章编号：1001-3695（2025）06-018-1734-08

doi：10.19734/j. issn.1001-3695.2024.10.0457

Multi-task cross-modal learning approach for out-of-distribution detection of emotional stress

Wan Yichen 1，2 ， Xing Kai 1，2† ，Liu Yu³，Yang Hui4，Xu Junhan1²，Yuan Yanxue 1，2 （20 （1.SchoolofomputerSiee &Tcholog，Uniersitofcince&TholfCina，Hefe6，Cha.SuzhostuefrAd vancedResearch，Unierstyofience&echologfina，SuzouJangsu5o，hina；3NnjingDrumowrHsptal，jing 210008，China；4.SchoolofLife Sciences，NorthwesternPolytechnical University，Xi’an71oo72，China）

Abstract：Recent research indicates that emotional stress detectionsystemsbasedon PPG signalscan bea potential convenient solution.However，PPG-based methods usuallyinduce severe OOD issues when detecting stressin previouslyunseen subjects duetosignificantvariations inPPGsignalsacross individuals.Toaddressthischallnge，thispaperproposedarossmodal stressdetection model basedonmulti-task learning（CSMT）.Byintroducing ECG signalreconstructionand multiple cardiovascular feature prediction asauxiliarytasks toenhancethefeatureextractioncapabilityofPPG signals，theproposed methodperformedcollaborativeoptimizationofPPG-based stress detectioninhigh-dimensionalrepresentationspace，thereby learning robuststressdetectionrepresentationsacrossindividuals.Experimentalresultsonthe WESADdatasetdemonstrate that inleave-one-subject-out validation tests，CSMT achieves best accuracy and F1 scores compared to existing methods in both thre-class （neutral/stress/amusement）andbinary（stress/non-stress）clasificationtasks，meanwhileefectivelymitigating theOODgeneralization probleminstressdetection.Theablation experimentsfurthervalidatetheefectiveness ofCSMTin enhancing model generalization capability.

Key Words：multi-task learning；photoplethysmography（PPG）； stress detection; out-of-distribution（OOD）issues

0 引言

情绪压力是一种复杂的生理心理反应，长期或慢性的情绪压力会对人体的呼吸系统、心血管系统及内分泌系统等产生严重的不良影响[1]，增加患焦虑症、抑郁症、心脏病以及注意力障碍等身心健康问题的风险[2]。（剩余20369字）