{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T16:46:11Z","timestamp":1768236371096,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42261068"],"award-info":[{"award-number":["42261068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20242BAB25112"],"award-info":[{"award-number":["20242BAB25112"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industry-University-Research Collaborative Education Project of the Ministry of Education of China","award":["220800247091048"],"award-info":[{"award-number":["220800247091048"]}]},{"name":"Graduate Education Reform Project of Jiaxing University","award":["651124009"],"award-info":[{"award-number":["651124009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Depression is a mental illness with hidden characteristics that affects human physical and mental health. In severe cases, it may lead to suicidal behavior (for example, among college students and social groups). Therefore, it has attracted widespread attention. Scholars have developed numerous models and methods for depression detection. However, most of these methods focus on a single modality and do not consider the influence of gender on depression, while the existing models have limitations such as complex structures. To solve this problem, we propose a symmetric-structured, multi-modal, multi-layer cooperative perception model for depression detection that dynamically focuses on critical features. First, the double-branch symmetric structure of the proposed model is designed to account for gender-based variations in emotional factors. Second, we introduce a stacked multi-head attention (MHA) module and an interactive cross-attention module to comprehensively extract key features while suppressing irrelevant information. A bidirectional long short-term memory network (BiLSTM) module enhances depression detection accuracy. To verify the effectiveness and feasibility of the model, we conducted a series of experiments using the proposed method on the AVEC 2014 dataset. Compared with the most advanced HMTL-IMHAFF model, our model improves the accuracy by 0.0308. The results indicate that the proposed framework demonstrates superior performance.<\/jats:p>","DOI":"10.3390\/informatics13010008","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T12:44:44Z","timestamp":1768221884000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Depression Detection Method Based on Multi-Modal Multi-Layer Collaborative Perception Attention Mechanism of Symmetric Structure"],"prefix":"10.3390","volume":"13","author":[{"given":"Shaorong","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Marxism, Jiaxing University, Jiaxing 314000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3674-8670","authenticated-orcid":false,"given":"Chengjun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Xiuya","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Marxism, Jiaxing University, Jiaxing 314000, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51208","DOI":"10.1109\/ACCESS.2025.3551549","article-title":"Hierarchical Multi-Task Learning Based on Interactive Multi-Head Attention Feature Fusion for Speech Depression Recognition","volume":"13","author":"Xing","year":"2025","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Brookman, R., Kalashnikova, M., Conti, J., Rattanasone, N., Grant, K., Demuth, K., and Burnham, D. (2020). Maternal depression affects infants\u2019 lexical processing abilities in the second year of life. Brain Sci., 10.","DOI":"10.3390\/brainsci10120977"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Luo, L., Yuan, J., Wu, C., Wang, Y., Zhu, R., Xu, H., Zhang, L., and Zhang, Z. (2025). Predictors of Depression among Chinese College Students: A Machine Learning Approach. BMC Public Health, 25.","DOI":"10.1186\/s12889-025-21632-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/TAFFC.2019.2927337","article-title":"Review on psychological stress detection using biosignals","volume":"13","author":"Giannakakis","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_5","unstructured":"Schwartz, M.S., and Andrasik, F. (2017). Biofeedback: A Practitioner\u2019s Guide, Guilford Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100587","DOI":"10.1016\/j.measen.2022.100587","article-title":"Hybrid Model for Depression Detection Using Deep Learning","volume":"25","author":"Marriwala","year":"2023","journal-title":"Meas. Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, X., Rong, L., and Tiwari, P. (2021, January 9\u201312). Multi-task learning for jointly detecting depression and emotion. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669546"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TAFFC.2020.3031345","article-title":"Multimodal spatiotemporal representation for automatic depression level detection","volume":"14","author":"Niu","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2340004","DOI":"10.1142\/S0218213023400043","article-title":"An effective depression diagnostic system using speech signal analysis through deep learning methods","volume":"32","author":"Verma","year":"2023","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s11136-018-2050-x","article-title":"How \u201cdepressed\u201d is \u201cdepressed\u201d? A systematic review and diagnostic meta-analysis of the optimal cut-off points of the revised Beck Depression Inventory (BDI-II)","volume":"28","author":"Hirschfeld","year":"2019","journal-title":"Qual. Life Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ramos-Vera, C., Quispe-Callo, G., Bashualdo-Delgado, M., Vallejos-Saldarriaga, J., and Santill\u00e1n, J. (2023). Factorial and network structure ofthe Reynolds Adolescent Depression Scale (RADS-2) in Peruvian adolescents. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0286081"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kraepelin, E. (1921). Manic-Depressive Insanity and Paranoia, E & S Livingstone.","DOI":"10.1097\/00005053-192104000-00057"},{"key":"ref_13","first-page":"407","article-title":"Advances in the Application of Wearable Devices in Depression Monitoring and Intervention","volume":"48","author":"He","year":"2024","journal-title":"Chin. J. Med. Devices"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/TAFFC.2025.3547753","article-title":"Detecting Stress Levels in College Students Using Affective Pulse Signals and Deep Learning","volume":"16","author":"Li","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7873","DOI":"10.1007\/s10586-017-1469-0","article-title":"Depression Detection Algorithm Combining Prosody and Sparse Face Recognition","volume":"22","author":"Zhao","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Amanat, A., Rizwan, M., Javed, A.R., Alsaqour, R., Pandya, S., and Uddin, M. (2022). Deep Learning for Depression Detection from Textual Data. Electronics, 11.","DOI":"10.3390\/electronics11050676"},{"key":"ref_17","unstructured":"Wongkoblap, A., Vadillo, M., and Curcin, V. (JMIR Ment. Health, 2021). Depression Detection of Twitter Posters using Deep Learning with Anaphora Resolution: Algorithm Development and Validation, JMIR Ment. Health, in press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/TAFFC.2018.2870884","article-title":"Video-based depression level analysis by encoding deep spatiotemporal features","volume":"12","author":"Guo","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.inffus.2021.10.012","article-title":"Deep Learning for Depression Recognition Using Audio-Visual Cues: A Review","volume":"80","author":"He","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/TCDS.2017.2721552","article-title":"Artificial intelligent system for automatic depression level analysis through visual and vocal expressions","volume":"10","author":"Jan","year":"2018","journal-title":"IEEE Trans. Cognit. Develop. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10.","DOI":"10.3390\/electronics10202470"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102161","DOI":"10.1016\/j.inffus.2023.102161","article-title":"Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals","volume":"104","author":"Fan","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37913","DOI":"10.1109\/ACCESS.2025.3545587","article-title":"Multi-Modal Fusion Attention Network for Depression Level Recognition Based on Enhanced Audio-Visual Cues","volume":"13","author":"Zhou","year":"2025","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106552","DOI":"10.1016\/j.bspc.2024.106552","article-title":"A novel multimodal depression diagnosis approach utilizing a new hybrid fusion method","volume":"96","author":"Zhang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mahayossanunt, Y., Nupairoj, N., Hemrungrojn, S., and Vateekul, P. (2023). Explainable depression detection based on facial expression u sing LSTM on attentional intermediate feature fusion with label smoothing. Sensors, 23.","DOI":"10.3390\/s23239402"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"123834","DOI":"10.1016\/j.eswa.2024.123834","article-title":"Attention-based CNN-BiLSTM model for depression detection from social media text","volume":"249","author":"Thekkekara","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Botalb, A., Moinuddin, M., Al-Saggaf, U.M., and Ali, S.S.A. (2018, January 13\u201314). Contrasting Convolutional Neural Network (CNN) with Multi-Layer Perceptron (MLP) for Big Data Analysis. Proceedings of the 2018 International Conference on Intelligent and Advanced System (ICIAS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIAS.2018.8540626"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"AbdelRaouf, H., Abouyoussef, M., and Ibrahem, M.I. (2024, January 18\u201320). An Innovative Approach for Human Activity Recognition Based on a Multi-Head Attention Mechanism. Proceedings of the 2024 International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA61862.2024.00240"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"73992","DOI":"10.1109\/ACCESS.2020.2988550","article-title":"Sentiment Classification Using a Single-Layered BiLSTM Model","volume":"8","author":"Hameed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhu, G., and Shu, J. (2022). A Combination of Lie Group Machine Learning and Deep Learning for Remote Sensing Scene Classification Using Multi-Layer Heterogeneous Feature Extraction and Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14061445"},{"key":"ref_31","first-page":"6056","article-title":"A Novel Driver Distraction Detection Method Based on Masked Image Modeling for Self-Supervised Learning","volume":"11","author":"Zhang","year":"2024","journal-title":"IEEE IoT J."},{"key":"ref_32","first-page":"1","article-title":"Anatomy of Breast Cancer Detection and Diagnosis Using Multilayer Perceptron Neural Network (MLP) and Convolutional Neural Network (CNN)","volume":"4","author":"Desai","year":"2021","journal-title":"Clin. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, C., Shu, J., and Zhu, G. (2023). Adversarial Remote Sensing Scene Classification Based on Lie Group Feature Learning. Remote Sens., 15.","DOI":"10.3390\/rs15040914"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134113","DOI":"10.1109\/ACCESS.2022.3231884","article-title":"Diagnosis of Depression Based on Four-Stream Model of Bi-LSTM and CNN From Audio and Text Information","volume":"10","author":"Jo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, L., Chen, X., Shen, Y., and Zhang, L. (2020). Towards automatic depression detection: A BiLSTM\/1D CNN-based model. Appl. Sci., 10.","DOI":"10.3390\/app10238701"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Valstar, M., Schuller, B., Smith, K., Almaev, T., Eyben, F., Krajewski, J., Cowie, R., and AVEC, M.P. (2014, January 7). 2014: 3D dimensional affect anddepression recognition challenge. Proceedings of the 4th International Workshop on Audio\/Visual Emotion Challenge, Orlando, FL, USA.","DOI":"10.1145\/2661806.2661807"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1109\/TAFFC.2022.3177737","article-title":"Dual attention and element recalibration networks for automatic depression level prediction","volume":"14","author":"Niu","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCDS.2023.3273614","article-title":"Spatial\u2013temporal feature network for speech-based depression recognition","volume":"1","author":"Han","year":"2024","journal-title":"IEEE Trans. Cognit. Develop. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"161203","DOI":"10.1109\/ACCESS.2024.3488081","article-title":"Integrating Bert With CNN and BiLSTM for Explainable Detection of Depression in Social Media Contents","volume":"12","author":"Cao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105898","DOI":"10.1016\/j.bspc.2023.105898","article-title":"A deep learning model for depression detection based on MFCC and CNN generated spectrogram features","volume":"90","author":"Das","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Liang, Z., Du, J., Zhang, L., Liu, C., and Zhao, L. (2021). Multi-head attention-based long short-term memory for depression detection from speech. Front. Neurorobotics, 15.","DOI":"10.3389\/fnbot.2021.684037"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T13:26:58Z","timestamp":1768224418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["informatics13010008"],"URL":"https:\/\/doi.org\/10.3390\/informatics13010008","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]}}}