research-article

Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes

Authors:

Shengfu LuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 2

Article No.: 42, Pages 1 - 22

https://doi.org/10.1145/3513263

Published: 25 September 2023 Publication History

Abstract

At present, there have been many studies on the methods of using the deep learning regression model to assess depression level based on behavioral signals (facial expression, speech, and language); however, the research on the assessment method of anxiety level using deep learning is absent. In this article, pupil-wave, a physiological signal collected by Human Computer Interaction (HCI) that can directly represent the emotional state, is developed to assess the level of depression and anxiety for the first time. In order to distinguish between different depression and anxiety levels, we use the HCI method to induce the participants’ emotional experience through three virtual reality (VR) emotional scenes of joyful, sad, and calm, and construct two differential pupil-waves of joyful and sad with the calm pupil-wave as the baseline. Correspondingly, a dual-channel fusion depression and anxiety level assessment model is constructed using the improved multi-scale convolution module and our proposed width-channel attention module for one-dimensional signal processing. The test results show that the MAE/RMSE of the depression and anxiety level assessment method proposed in this article is 3.05/4.11 and 2.49/1.85, respectively, which has better assessment performance than other related research methods. This study provides an automatic assessment technique based on human computer interaction and virtual reality for mental health physical examination.

References

[1]

Paul E. Greenberg, Andree-Anne Fournier, Tammy Sisitsky, Crystal T. Pike, and Ronald C. Kessler. 2015. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry 76, 2 (2015), 155–162.

[2]

Theo Vos, Ryan M. Barber, Brad Bell, Amelia Bertozzi-Villa, Stan Biryukov, Ian Bolliger, Fiona Charlson, Adrian Davis, Louisa Degenhardt, Daniel Dicker et al. 2015. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the global burden of disease study 2013. The Lancet 386, 9995 (2015), 743–800.

[3]

C. Mathers, D. M. Fat, and J. T. Boerma. 2008. The Global Burden of Disease: 2004 Update. Geneva, Switzerland: World Health Organization.

[4]

Ronald C. Kessler, Hanna M. van Loo, Klaas J. Wardenaar, and Robert M. Bossarte. 2017. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiology and Psychiatric Sciences 26, 1 (2017), 22–36.

[5]

Malcolm Lader. 2015. Generalized anxiety disorder. In Encyclopedia of Psychopharmacology. Berlin, Germany, Springer, 699–702.

[6]

Aaron T. Beck, Robert A. Steer, Roberta Ball, and William F. Ranieri. 1996. Comparison of beck depression inventories-I and-Ⅱ in psychiatric outpatients. Journal of Personality Assessment 67, 3 (1996), 588–597.

[7]

Kurt Kroenke, Tara W. Strine, Robert L. Spitzer, Janet B. W. Williams, Joyce T. Berry, and Ali H. Mokdad. 2009. The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders 114, 1–3 (2009), 163–173.

[8]

Kurt Kroenke and Robert L. Spitzer. 2002. The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals 32, 9 (2002), 509–521.

[9]

Robert L. Spitzer, Kurt Kroenke, Janet B. W. Williams, and Bernd Löwe. 2006. A brief measure for assessing generalized anxiety disorder. Archives of Internal Medicine 166, 10 (2006), 1092–1097.

[10]

Stuart K. Card and Moran P. Thomas. 2008. The Psychology of Human Computer Interaction. CRC Press.

[11]

Steuer Jonathan. 2010. Defining virtual reality: Dimensions determining telepresence. Journal of Communication 42, 4 (2010).

[12]

Johanna M. Baas, Monique Nugent, Shmuel Lissek, Daniel S. Pine, and Christian Grillon. 2004. Fear conditioning in virtual reality contexts: A new tool for the study of anxiety. Biological Psychiatry 55, 11 (2004), 1056–1060.

[13]

Kyle E. Madsen. 2016. The differential effects of agency on fear induction using a horror-themed video game. Computers in Human Behavior 56, (2016), 142–146.

Digital Library

[14]

Le Yang, Dongmei Jiang, and Lang He. 2016. Decision tree based depression classification from audio video and language information. In Proceedings of the ACM Multimedia Conference Amsterdam the Netherlands. 89–96.

[15]

Yu Zhu, Yuanyuan Shang, Zhuhong Shao, and Guodong Guo. 2018. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Transactions on Affective Computing 9, 4 (2018), 578–584.

Digital Library

[16]

Lang He and Cui Cao. 2018. Automated depression analysis using convolutional neural networks from speech. Journal of Biomedical Informatics 83, (2018), 103–111.

[17]

Albert Haque, Michelle Guo, Adam S. Mine, and Li Fei-Fei. 2018. Measuring depression symptom severity from spoken language and 3D facial expressions. arXiv preprint arXiv:1811.08592 (2018).

[18]

Tuka Al Hanai, Mohammad Ghassemi, and James R. Glass. 2018. Detecting depression with audio/text sequence modeling of interviews. In Proceedings of the Interspeech 2018. 1716–1720.

[19]

James R. Williamson, Elizabeth Godoy, Miriam Cha, Adrianne Schwarzentruber, Pooya Khorrami, Youngjune Gwon, Hsiang-Tsung Kung, Charlie Dagli, and Thomas F. Quatieri. 2016. Detecting depression using vocal, facial and semantic communication cues. In Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge. 11–18.

[20]

Le Yang, Dongmei Jiang, and Hichem Sahli. 2018. Integrating deep and shallow models for multi-modal depression analysis-hybrid architectures. IEEE Transactions on Affective Computing 12, 1 (2018), 239–253.

[21]

Ziping Zhao, Zhongtian Bao, Zixing Zhang, Jun Deng, Nicholas Cummins, Haishuai Wang, Jianhua Tao, and Björn Schuller. 2020. Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders. IEEE Journal of Selected Topics in Signal Processing 14, 2 (2020), 423–434.

[22]

Wheidima Carneiro de Melo, Eric Granger, and Abdenour Hadid. 2019. Combining global and local convolutional 3D networks for detecting depression from facial expressions. In Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition. 1–8.

[23]

Yuan Gong and Christian Poellabauer. Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions.

[24]

Pieter Desmet. 2018. Measuring emotion: Development and application of an instrument to measure emotional responses to products. In Funology. Berlin, Germany, Springer, 111–123.

[25]

Manon Mulckhuyse. 2018. The influence of emotional stimuli on the oculomotor system: A review of the literature. Cognitive Affective & Behavioral Neuroscience 18, 2 (2018), 411–415.

[26]

Margaret M. Bradley, Laura Miccoli, Miguel A. Escrig, and Peter J. Lang. 2008. The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, 4 (2008), 602–607.

[27]

Rosa M. Baños, Cristina Botella, Isabel Rubió, Soledad Quero, Azucena García-Palacios, and Mariano Alcañiz. 2008. Presence and emotions in virtual environments: The influence of stereoscopy. Cyberpsychology & Behavior 11, 1 (2008), 1–8.

[28]

Rosa María Baños, Víctor Liaño, Cristina Botella, Mariano Alcañiz, Cristina Botella, and Beatriz Rey. 2006. Changing induced moods via virtual reality. In Proceedings of the 1st International Conference on Persuasive Technology for Human Well-being. 7–15.

[29]

Christos D. Katsis, Nikolaos S. Katertsidis, and Dimitrios I. Fotiadis. 2011. An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomedical Signal Processing and Control 6, 3 (2011), 261–268.

[30]

Moitreya Chatterjee, Giota Stratou, Stefan Scherer, and Louis-Philippe Morency. 2014. Context-based signal descriptors of heart-rate variability for anxiety assessment. In Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’14). 3631–3635.

[31]

Wahidah Husain, Lee Ker Xin, Nur'Aini Abdul Rashid, and Neesha Jothi. 2016. Predicting generalized anxiety disorder among women using random forest approach. In Proceedings of the 2016 3rd International Conference on Computer and Information Sciences (ICCOINS’16). 37–42.

[32]

Kevin Hilbert, Ulrike Lueken, Markus Muehlhan, and Katja Beesdo-Baum. 2017. Separating generalized anxiety disorder from major depression using clinical. Hormonal, and structural MRI data: A multimodal machine learning study. Brain and Behavior 12, 3 (2017), e00633.

[33]

Susan Nolen-Hoeksema. 2000. The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology 109, 3 (2000), 504–511.

[34]

Catherine Donaldson, Dominic Lam, and Andrew Mathews. 2007. Rumination and attention in major depression. Behaviour Research and Therapy 45, 11 (2007), 2664–2678.

[35]

Maximilian Christ, Andreas W. Kempa-Liehr, and Michael Feindt. 2016. Distributed and parallel time series feature extraction for industrial big data applications. arXiv preprint arXiv:1610.07717 (2016).

[36]

Anastasia Pampouchidou, Panagiotis G. Simos, Kostas Marias, Fabrice Meriaudeau, Fan Yang, Matthew Pediaditis, and Manolis Tsiknakis. 2019. Automatic assessment of depression based on visual cues: A systematic review. IEEE Transactions on Affective Computing 10, 4 (2019), 445–470.

[37]

Michel Valstar, Björn Schuller, Kirsty Smith, Florian Eyben, Bihan Jiang, Sanjay Bilakhia, Sebastian Schnieder, Roddy Cowie, and Maja Pantic. 2013. AVEC 2013: The continuous audio/visual emotion and depression recognition challenge. In Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge (AVEC'13). 3–10.

[38]

Michel Valstar, Björn Schuller, Kirsty Smith, Timur Almaev, Florian Eyben, Jarek Krajewski, Roddy Cowie, and Maja Pantic. 2014. AVEC 2014: 3D dimensional affect and depression recognition challenge. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (AVEC'14). 3–10.

[39]

Michel Valstar, Jonathan Gratch, Björn Schuller, Fabien Ringeval, Denis Lalanne, Mercedes Torres Torres, Stefan Scherer, Giota Stratou, Roddy Cowie, and Maja Pantic. 2016. AVEC 2016: Depression, mood, and emotion recognition workshop and challenge. In Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge (AVEC'16). 3–10.

[40]

Fabien Ringeval, Björn Schuller, Michel Valstar, Jonathan Gratch, Roddy Cowie, Stefan Scherer, Sharon Mozgai, Nicholas Cummins, Maximilian Schmitt, and Maja Pantic. 2017. AVEC 2017: Real-life depression, and affect recognition workshop and challenge. In Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge (AVEC'17). 3–9.

[41]

Asim Jan, Hongying Meng, Yona Falinie Binti A. Gaus, and Fan Zhang. 2018. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Transactions on Cognitive and Developmental Systems 10, 3 (2018), 668–680.

[42]

Mohamad Al Jazaery and Guodong Guo. 2021. Video-based depression level analysis by encoding deep spatiotemporal features. IEEE Transactions on Affective Computing 12, 1 (2018), 262–268.

[43]

Xiuzhuang Zhou, Kai Jin, Yuanyuan Shang, and Guodong Guo. 2020. Visually interpretable representation learning for depression recognition from facial images. IEEE Transactions on Affective Computing 11, 3 (2020), 542–552.

[44]

Muhammad Muzammel, Hanan Salam, Yann Hoffmann, Mohamed Chetouani, and Alice Othmani. 2020. AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis. Machine Learning with Applications 2, (2020), 100005.

[45]

Lang Hea and Cui Caob. 2018. Automated depression analysis using convolutional neural networks from speech. Journal of Biomedical Informatics 83, (2018), 103–111.

[46]

Mi Li, Shengfu Lu, Gang Wang, Lei Feng, Bingbing Fu, and Ning Zhong. 2016. Emotion, working memory, and cognitive control in patients with first-onset and previously untreated minor depressive disorders. Journal of International Medical Research 44, 3 (2016), 529–541.

[47]

Mi Li, Shengfu Lu, Lei Feng, Bingbing Fu, Gang Wang, Ning Zhong, and Bin Hu. 2016. Emotional experience and the mood-congruent working memory effect in first-onset and untreated depressive disorder patients. Journal of Psychiatry 19, 4 (2016), 1000379.

[48]

Nicholas Cummins, Stefan Scherer, Jarek Krajewski, Sebastian Schnieder, Julien Epps, and Thomas F. Quatieri. 2015. A review of depression and suicide risk assessment using speech analysis. Speech Communication 71, (2015), 10–49.

Digital Library

[49]

Jeffrey M. Girard and Jeffrey F. Cohn. 2015. Automated audiovisual depression analysis. Current Opinion in Psychology 4, (2015), 75–79.

[50]

Anastasia Pampouchidou, Panagiotis G. Simos, Kostas Marias, Fabrice Meriaudeau, Fan Yang, Matthew Pediaditis, and Manolis Tsiknakis. 2019. Automatic assessment of depression based on visual cues: A systematic review. IEEE Transactions on Affective Computing 10, 4 (2019), 445–470.

[51]

Hamdi Dibeklioglu, Zakia Hammal, and Jeffrey F. Cohn. 2018. Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE Journal of Biomedical and Health Informatics 22, 2 (2018), 525–536.

[52]

Oliver Doehrmann, Satrajit S. Ghosh, Frida E. Polli, Gretchen O. Reynolds, Franziska Horn, Anisha Keshavan, Christina Triantafyllou, Zeynep M. Saygin, Susan Whitfield-Gabrieli, Stefan G. Hofmann, Mark Pollack, and John D. Gabrieli. 2013. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry 70, 1 (2013), 87–97.

[53]

S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman. 2021. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing 151 (2021), 107398.

[54]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems-Volume 1 (NIPS'12). 1097–1105.

[55]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

[56]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelo, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 1–9.

[57]

Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr. 2021. Res2Net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 2 (2021), 652–662.

Digital Library

[58]

Pengcheng Yuan, Shufei Lin, Cheng Cui, Yuning Du, Ruoyu Guo, Dongliang He, Errui Ding, and Shumin Han. 2020. HS-ResNet: Hierarchical-split block on convolutional neural network. arXiv preprint arXiv:2010.07621 (2020).

[59]

Dzmitry Bahdanau, Jan Chorowski, Dmitriy Serdyuk, Philémon Brakel, and Yoshua Bengio. 2016. End-to-end attention-based large vocabulary speech recognition. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’16). 4945–4949.

[60]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). 6000–6010.

[61]

Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu. 2020. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 42, 8 (2020), 2011–2023.

Digital Library

[62]

Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu. 2019. ECA-Net: Efficient channel attention for deep convolutional neural networks. arXiv preprint arXiv:1910.03151 (2019).

[63]

Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. CBAM: Convolutional block attention module. In Proceedings of the 2018 European Conference on Computer Vision. 3–19.

[64]

Xiaolong Wang, Ross Girshick, and Harikrishna Mulam. 2018. Non-local neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7794–7803.

[65]

Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. 2019. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3146–3154.

[66]

Ionut Cosmin Duta, Li Liu, Fan Zhu, and Ling Shao. 2020. Pyramidal convolution: Rethinking convolutional neural networks for visual recognition. arXiv preprint arXiv:2006.11538 (2020).

[67]

Farmanullah Jan. 2018. Pupil localization in image data acquired with near-infrared or visible wavelength illumination. Multimedia Tools and Applications 77, 1 (2018), 1041–1067.

Digital Library

[68]

A. R. Kiruthiga and R. Arumuganathan. 2017. Smoothening of iris images and pupil segmentation using fractional derivative and wavelet transform. In Proceedings of the 2017 4th International Conference on Signal Processing, Communication and Networking (ICSCN’17). 1–6.

[69]

Thiago Santini, Wolfgang Fuhl, and Enkelejda Kasneci. 2018. PuRe: Robust pupil detection for real-time pervasive eye tracking. Computer Vision and Image Understanding 170, 4 (2018), 40–50.

Digital Library

[70]

Danjie Zhu, Steven T. Moore, and Theodore Raphan. 1999. Robust pupil center detection using a curvature algorithm. Computer Methods and Programs in Biomedicine 59, 3 (1999), 145–157.

[71]

Andrew T. Duchowski. 2007. Eye tracking methodology. Theory and Practice 328, 614 (2007), 2–3.

[72]

Hazim G. Daway, Hana H. Kareem, and Ahmed Rafid Hashim. 2018. Pupil detection based on color difference and circular though transform. International Journal of Electrical and Computer Engineering 8, 5 (2018), 3278–3284.

[73]

Ashwaq T. Hashim and Duaa A. Noori. 2016. An approach of noisy color iris segmentation based on hybrid image processing techniques. In Proceedings of the 2016 International Conference on Cyberworlds (CW’16). 183–188.

[74]

Chen Junfen and Yan Li. 2015. Refining Width Parameter of Gaussian Kernel Function for Tuning Coherent Point Drift (CPD) Registration. (2015).

[75]

S. Gopal Krishna Patro and Kishore Kumar Sahu. 2015. Normalization: A preprocessing stage. International Advanced Research Journal in Science, Engineering and Technology 2, 3 (2015), 20–22.

[76]

Qibin Hou, Daquan Zhou, and Jiashi Feng. 2021. Coordinate attention for efficient mobile network design. arXiv preprint arXiv:2103.02907 (2021).

[77]

Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang. 2019. Selective kernel networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 510–519.

[78]

Le Yang, Dongmei Jiang, Xiaohan Xia, and Ercheng Pei. 2017. Multimodal measurement of depression using deep learning models. In Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge.

Cited By

Sánchez Pineda CJaramillo Valbuena STriviño J(2024)Capítulo 4: Aplicación de Redes Neuronales para clasificación de texto sobre entrevistas médicas del corpus DAIC-WoZGestión del conocimiento. Perspectiva multidisciplinaria (libro 65)10.59899/Ges-cono-65-C4(65-85)Online publication date: 31-May-2024
https://doi.org/10.59899/Ges-cono-65-C4
Yuan BLu JYou SBao B(2024)Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674737Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674737
Sahu NGupta SLone H(2024)Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious ActivitiesACM Journal on Computing and Sustainable Societies10.1145/36636712:2(1-23)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3663671
Show More Cited By

Index Terms

Automatic Assessment of Depression and Anxiety through Encoding Pupil-wave from HCI in VR Scenes
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Automatic detection and classification of emotional states in virtual reality and standard environments (LCD): comparing valence and arousal of induced emotions
Abstract
The following case study was carried out on a sample of one experimental and one control group. The participants of the experimental group watched the movie section from the standardized LATEMO-E database via virtual reality (VR) on Oculus Rift S ...
Reflecting on Emotions within VR Mood Worlds
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Virtual reality applications can be an effective tool for emotion regulation and therefore support emotional well-being and mental health. However, many VR applications focus on inducing (positive) emotions and there is little research on how users can ...
A widget framework for augmented interaction in SCAPE
UIST '03: Proceedings of the 16th annual ACM symposium on User interface software and technology

We have previously developed a collaborative infrastructure called SCAPE - an acronym for Stereoscopic Collaboration in Augmented and Projective Environments - that integrates the traditionally separate paradigms of virtual and augmented reality. In ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 2

February 2024

548 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3613570

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2023

Online AM: 30 April 2022

Accepted: 22 January 2022

Revised: 21 December 2021

Received: 15 October 2021

Published in TOMM Volume 20, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China
National Basic Research Programme of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
1,206
Total Downloads

Downloads (Last 12 months)945
Downloads (Last 6 weeks)100

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sánchez Pineda CJaramillo Valbuena STriviño J(2024)Capítulo 4: Aplicación de Redes Neuronales para clasificación de texto sobre entrevistas médicas del corpus DAIC-WoZGestión del conocimiento. Perspectiva multidisciplinaria (libro 65)10.59899/Ges-cono-65-C4(65-85)Online publication date: 31-May-2024
https://doi.org/10.59899/Ges-cono-65-C4
Yuan BLu JYou SBao B(2024)Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674737Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674737
Sahu NGupta SLone H(2024)Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious ActivitiesACM Journal on Computing and Sustainable Societies10.1145/36636712:2(1-23)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3663671
Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727
Liu HYuan RLv YYang XLi HSong G(2024)Degradation Tracking of Rolling Bearings Based on Local Polynomial Phase Space WarpingIEEE Transactions on Reliability10.1109/TR.2023.333589973:2(1380-1392)Online publication date: Jun-2024
https://doi.org/10.1109/TR.2023.3335899
Deng WChen XLi XZhao H(2024)Adaptive Federated Learning With Negative Inner Product AggregationIEEE Internet of Things Journal10.1109/JIOT.2023.331205911:4(6570-6581)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3312059
Afjal MMondal MMamun M(2024)Effective hyperspectral image classification based on segmented PCA and 3D-2D CNN leveraging multibranch feature fusionJournal of Spatial Science10.1080/14498596.2024.2305119(1-28)Online publication date: 30-Jan-2024
https://doi.org/10.1080/14498596.2024.2305119
Zhang XYang XHuang CCao JLiu H(2024)Adaptive fuzzy finite-time PID backstepping control for chaotic systems with full states constraints and unmodeled dynamicsInformation Sciences10.1016/j.ins.2024.120148661(120148)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120148
Zhu ZLi XChen HZhou XDeng W(2024)An effective and robust genetic algorithm with hybrid multi-strategy and mechanism for airport gate allocationInformation Sciences10.1016/j.ins.2023.119892654(119892)Online publication date: Jan-2024
https://doi.org/10.1016/j.ins.2023.119892
Zhou ZGeng CQi BMeng AXiao J(2023)Research and experiment on global path planning for indoor AGV via improved ACO and fuzzy DWAMathematical Biosciences and Engineering10.3934/mbe.202384620:11(19152-19173)Online publication date: 2023
https://doi.org/10.3934/mbe.2023846
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents