Pith. sign in

REVIEW 5 major objections 6 minor 55 references

StressGAT, a graph attention network over facial action-unit segments, reaches 88.62% accuracy in subject-independent stress recognition.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

StressGAT reports 88.62% LOSO accuracy for stress-vs-neutral from temporal graph attention over 10-s segments of facial Action Units (58 subjects), plus two claimed expressivity phenotypes — but the differential-AU normalization named in the title is not defined in the method.

T0 review reviewed 2026-08-01 challenge →

load-bearing objection Competent GAT-based stress recognition paper whose headline mechanism — Differential Action Unit personalization — is never actually defined or shown in the pipeline, so the 88.62% result can't be attributed to it as written. the 5 major comments →

arxiv 2607.20819 v2 pith:HL2535PX submitted 2026-07-23 cs.CV

Explainable graph attention network for stress recognition (StressGAT) via differential action units

Thomas Kassiotis , Stefanos Gkikas , Nikolaos Smyrnis , Giorgos Giannakakis This is my paper
classification cs.CV
keywords StressGATstress detectiongraph neural networksfacial action unitsexplainable AImultiple instance learningleave-one-subject-outaffective computing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

StressGAT argues that acute stress leaves a readable trace in facial action-unit dynamics, but only if those dynamics are modeled as a directed, causal temporal graph rather than as isolated frames or a flat sequence. The paper claims that this graph attention architecture, with multiple-instance-learning attention pooling, reaches 88.62% accuracy and an F1 of 0.89 under leave-one-subject-out evaluation on 58 people, beating SVM, LSTM, GRU, 1D-CNN, and Transformer baselines. It also claims that the attention weights make predictions interpretable, identifying which facial muscles and which 10-second segments drive each decision, and that clustering those importance vectors reveals two stress phenotypes: an expressive, upper-face externalizing group and a suppressive, perioral internalizing group. Why it matters: if these claims hold, contact-free, personalized stress monitoring becomes plausible with a model whose decisions a clinician can audit.

Core claim

The central claim, stated in the paper's own terms, is that representing each subject's facial behavior as a sequence of 10-second nodes with per-action-unit mean and standard deviation features, connected by a strictly causal graph and processed by GATv2 attention, yields a classification model that is both more accurate and more transparent than existing deep approaches. Reported results: 88.62% mean accuracy, 0.89 F1, AUC 0.907 under LOSO; removal of the temporal edges drops accuracy to 84.52%, confirming that the temporal topology carries real signal. The MIL attention layer produces per-segment importance weights, which the paper uses both to locate peak-stress intervals and to derive p

What carries the argument

The load-bearing machinery is the directed causal temporal graph G=(V,E) with N=12 nodes (one per 10-second segment) and edges (vj,vi) whenever 0<i-j≤ω, where ω is a look-back window left unspecified in the paper. Each node carries a 34-dimensional vector, the concatenation of the per-AU means and standard deviations across the segment's roughly 300 frames. Two GATv2 convolution layers compute dynamic attention coefficients e_ij = a^T LeakyReLU(W[h_i || h_j]) along the causal edges, propagating information only from the past to the present. A multiple-instance-learning pooling layer, implemented as a gated MLP with softmax weights, aggregates the 12 node embeddings into a single graph embedd

Load-bearing premise

The load-bearing premise is that the neutral-baseline subtraction promised by 'Differential Action Units' is actually applied before node features (means and standard deviations of each AU) are fed into the graph; the paper never defines that transform and the node features are described as raw statistics, so if the subtraction is absent, the personalization mechanism—and the claim that it drives the accuracy—collapses.

What would settle it

Read the code path that constructs node features for one subject. If the tensor entering the first GATv2 layer is exactly [mean(AU1..17) | std(AU1..17)] with no subject-specific reference subtraction, the personalization claim is falsified as implemented; a direct ablation comparing the model with and without the differential transform would settle whether the 88.62% depends on it at all. Independently, sweeping ω from 1 to 11 should change accuracy if the causal look-back carries signal; a flat curve would indicate the model is effectively a bag of segments.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Contact-free stress detection from facial video alone can reach subject-independent accuracy comparable to multi-sensor setups, at least in structured stress-induction protocols.
  • The causal temporal graph is a more efficient inductive bias than global self-attention for this task, matching or beating a Transformer with about 12% fewer parameters.
  • Model decisions can be traced to specific action units and time segments, giving clinicians a concrete artifact—the MIL attention weights—to audit rather than a black-box score.
  • The two-phenotype split implies that low-movement (suppressive) subjects will be systematically misclassified by most facial-expression systems, and that blink rate and perioral tension are measurable signals worth coding explicitly.
  • The reported error pattern (35 false negatives concentrated in suppressive subjects) implies that purely visual stress detection has a biological ceiling, so high-stakes use should fuse facial and physiological channels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the implementation feeds exactly the raw mean/std features defined in Section III-B, with no neutral-baseline subtraction, then the 'differential action unit' personalization is not present in the trained model; the reported accuracy would then be due solely to graph topology and pooling, and the personalized-recognition claim remains untested.
  • The causal look-back ω is never assigned a value, so the model's true temporal receptive field is unknown; a natural test is to sweep ω and report accuracy, which would show whether the model actually exploits long-range dependencies or just local adjacency.
  • The two-phenotype finding suggests a prospective clinical check: if suppressive subjects are identified by the model, their stress could be compared against physiological signals (e.g., heart rate or electrodermal response) to see whether their facial stillness is truly uninformative or merely harder to read.
  • A direct extension would be training StressGAT on naturalistic, continuous stress recordings rather than the task-segmented lab protocol; if the causal graph still separates stress from neutral under unsegmented video, the temporal-accumulation story is much stronger.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes StressGAT, a graph attention network for binary stress-versus-neutral classification from facial Action Unit (AU) time series. Frame-level AU intensities are aggregated into 10-second segments, represented by concatenated segment mean and standard deviation vectors, and arranged as nodes of a directed causal temporal graph processed by two GATv2 layers with MIL attentional pooling. On a 58-participant stress-induction cohort evaluated under Leave-One-Subject-Out cross-validation, the authors report 88.62% accuracy, F1 0.89, and AUC 0.907, outperforming SVM, LSTM, GRU, 1D-CNN, and Transformer baselines. The paper also presents an ablation removing temporal edges and a clustering analysis of model-derived AU importances, yielding two claimed stress phenotypes (Expressive and Suppressive). The title and abstract credit the results to 'Differential Action Units' that normalize features against neutral baselines, but this transform is never defined in the methodology.

Significance. If fully substantiated, the work would be a useful contribution to explainable, subject-independent facial stress recognition: the architecture is clearly specified, multiple independent baselines are compared under the same LOSO protocol, and the interpretability mechanisms are thoughtfully motivated. The GATv2 temporal-graph formulation with MIL pooling is a sensible design. However, the paper's central claimed mechanism—differential AU personalization—is not actually specified in the methodology, and the sensitivity analysis selects the temporal granularity on the test metric, which introduces optimism. The empirical result is therefore not yet attributable to the claimed method, making the current significance contingent on substantial clarification and re-analysis.

major comments (5)
  1. [III-B] The central claim that StressGAT uses 'Differential Action Units' to normalize individual responses relative to neutral baselines is never operationalized. The only description is the sentence 'The pairwise transformation used to normalize against neutral baselines is based on the framework described in [50]', followed immediately by node features defined as raw statistics: x_t = [μ_1..μ_17] ⊕ [σ_1..σ_17]. No subtraction, division, or any subject-specific baseline adjustment appears in the node construction or in Eqs. (1)–(5). As written, the model is a generic GAT on absolute AU moments, so the abstract, title, and introduction overstate what is implemented. Please specify the differential transform explicitly, state where in the pipeline it is applied, and either include it in the feature definition or revise the paper's claims accordingly.
  2. [III-C] The causal look-back window ω in Eq. (1) is never assigned a value. This parameter fully determines the graph's edge set, temporal receptive field, and the amount of context each node receives. Without specifying ω, the experimental setup is not reproducible and the effect of temporal topology cannot be assessed. The ablation in Section V-B removes all temporal edges, but does not vary ω. Please report the value used, justify it, and ideally include an ablation over ω.
  3. [V-A, Table II] The 10-second window / N=12 configuration is selected because it gives the best mean LOSO accuracy among five tested granularities (88.62% vs. 79–85% for others). This is a form of test-set model selection: the reported headline number is the maximum over a set of configurations evaluated on the same LOSO folds. The 88.62% is therefore optimistically biased. To support the headline claim, the authors should use nested cross-validation, or at minimum treat Table II as exploratory and report the final performance in a way that accounts for the selection, e.g., with corrected confidence intervals or a separate held-out test set.
  4. [Table VI] The claimed superiority over strong baselines is not supported by inferential statistics. The gap over 1D-CNN is 2.19 percentage points and over Transformer is 1.52 points, while fold standard deviations are around ±10–11 points. No paired significance tests, confidence intervals, or effect sizes are reported across the LOSO folds. Given N=58, a paired test across folds (e.g., Wilcoxon signed-rank or McNemar) is necessary to determine whether the differences are meaningful. Without this, the conclusion that StressGAT 'outperforms' the baselines is not statistically grounded.
  5. [V-C] The phenotype analysis clusters participants based on model-derived AU importance vectors, but the stability and validity of the two clusters are not established beyond a Silhouette score. No bootstrap, cluster reproducibility analysis, or comparison against clustering on raw AU statistics is provided. The labels 'Expressive (Externalizing)' and 'Suppressive (Internalizing)' are interpretative post-hoc assignments. Please present this section as exploratory and add appropriate caveats, or provide additional validation that the clusters are not artifacts of the model's attention weights.
minor comments (6)
  1. [V-A] Heading typo: 'Perfomance' should be 'Performance'.
  2. [Table III] The GATv2Conv parameter counts are not derivable from the stated input/output dimensions and number of heads. Please specify whether heads are concatenated or averaged, the attention vector dimension, and whether biases are included, so that the reported 56,935 total can be independently verified.
  3. [Table VIII] AU24 (Lip Pressor) is listed as a dominant AU for the Suppressive phenotype, but Table I and the feature extraction description list only 17 AUs and do not include AU24. Please reconcile which AUs were actually extracted and used.
  4. [Table IV] The protocol includes multiple neutral reference tasks (1.1, 2.1, 3.1, 4.1). If a differential AU normalization is intended, it is unclear which neutral segment serves as the baseline for each stress task. Please state this explicitly.
  5. [Figure 2] The ROC curve should specify whether it is averaged over LOSO folds, how the threshold is chosen, and what the error bars or fold spread are.
  6. [II] Several references are to 2026 arXiv preprints and self-citations. Please ensure the related-work discussion distinguishes peer-reviewed results from preprints, and consider adding standard earlier GNN-based AU modeling references.

Circularity Check

0 steps flagged

No significant circularity; the 88.62% LOSO result is benchmarked externally, with only a minor self-citation in the under-specified differential-AU preprocessing.

full rationale

The principal empirical claim (88.62% accuracy, F1 0.89, AUC 0.907) is an experimental result obtained under a subject-independent LOSO protocol and compared against seven independent baselines on the same data; no equation in the paper constructs this number from its own fitted parameters or from a previously asserted conclusion. The methodology is self-contained at the level of the implemented graph architecture: raw AU mean/std node features (Section III-B), causal adjacency Eq. (1), GATv2 attention Eqs. (2)-(5), MIL pooling, cross-entropy training. The one circularity-adjacent surface is the 'Differential Action Unit' personalization, which is specified only by the self-citation 'The pairwise transformation used to normalize against neutral baselines is based on the framework described in [50]' (Section III-B), while the node features are defined as [μ1..μ17]⊕[σ1..σ17]. If the transform is not applied, the claimed personalization mechanism is absent; this is an internal-consistency/reproducibility gap, not a reduction of the result to the model's inputs. The causal look-back window ω in Eq. (1) is likewise unassigned, a completeness issue rather than circularity. The phenotype clustering is post-hoc on model-derived AU importances and is not the source of the headline accuracy. Accordingly, no step satisfying the quoted-reduction criterion was found; the score reflects only the minor self-citation, not a circular derivation.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 2 invented entities

The ledger shows a typical ML-paper profile: the empirical claim rests on a small number of hand- or test-set-selected hyperparameters (window size N=12, the unstated look-back ω, cluster count k=2) and on domain assumptions that are cited rather than demonstrated (OpenFace fidelity, task-label validity, the [50] differential transform). No new physical entities are needed; the two invented 'phenotypes' are post-hoc labels on the model's own attention outputs and currently have no independent evidence. The largest uncharged debt is the differential-AU normalization from [50], a same-author prior framework that is never specified here yet carries the paper's personalization claim.

free parameters (4)
  • Temporal window / node count N = N=12 (10 s non-overlapping segments)
    Chosen by sensitivity analysis (Table II) maximizing the reported LOSO accuracy; this is selection on the evaluation protocol and inflates the headline 88.62%.
  • Causal look-back window ω (Eq. 1) = not specified anywhere in the paper
    Defines the temporal receptive field of the directed graph, yet no value is given; reproduction requires guessing it.
  • Number of phenotype clusters k = k=2 (silhouette peak)
    Cluster count selected by silhouette analysis on the model's own attention-importance vectors (n=58); the two-cluster choice is then interpreted as a biological dichotomy without external validation.
  • Training hyperparameters (lr, weight decay, hidden dims, heads) = lr=1e-3, wd=1e-4, 256/64 dims, 4 heads
    Hand-chosen standard values; architectural choices not derived from data. Minor relative to the window and ω.
axioms (5)
  • domain assumption OpenFace 2.0 AU-intensity estimates are accurate enough under pose/illumination variation for stress inference
    Invoked in Section III-A; the entire feature layer depends on the toolkit's CE-CLM estimates (refs [41]-[43]) being noise-free enough not to dominate the signal.
  • domain assumption Task-level labels are valid supervision for every 10-s segment of that task
    Each 2-minute task carries one label (stress/neutral, Table IV); the MIL layer is meant to handle within-task label noise, but accuracy is still computed against task labels.
  • domain assumption The differential-normalization framework of [50] (same-group prior work) is valid and applicable to this cohort
    Section III-B defers the central personalization mechanism to [50] without stating its equations, so the central claim inherits an unstated prior result with author overlap.
  • domain assumption Silhouette analysis on attention-importance vectors (n=58) supports k=2 phenotypes
    Section V-C treats the silhouette peak on 58 model-derived vectors as evidence for a biological dichotomy, without a validation set, physiological data, or a test of cluster stability.
  • domain assumption GATv2 attention is a suitable inductive bias for nonlinear stress dynamics
    Core architectural premise (Section III-E); a modeling choice, not derived.
invented entities (2)
  • 'Expressive (Externalizing)' stress phenotype no independent evidence
    purpose: Explains individual differences in facial stress expression (n=43 of 58)
    Derived by clustering the model's own AU-importance vectors; no physiological recording, self-report, or second cohort ties the cluster to human coping style rather than to model artifacts.
  • 'Suppressive (Internalizing)' stress phenotype no independent evidence
    purpose: Explains the minority pattern of near-zero facial movement during stress (n=15 of 58)
    Same derivation and same lack of external validation; the paper itself notes this group is 'kinematically indistinguishable' from neutral at the feature level, so the cluster rests entirely on model attention weights.

reviewed 2026-08-01 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Explainable graph attention network for stress recognition (StressGAT) via differential action units." pith.science (2026). https://pith.science/paper/HL2535PX

@misc{pith2026260720819,
  author       = {Pith},
  title        = {Pith review of: Explainable graph attention network for stress recognition (StressGAT) via differential action units},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HL2535PX}},
  note         = {Machine review of arXiv:2607.20819}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook person-specific baselines or lack the representational capacity to model the non-linear temporal progression of distress due to sequential bottlenecks and rigid grid-based constraints. Furthermore, many deep learning models lack the interpretability required for clinical deployment. This study introduces StressGAT, a Graph Attention Network that leverages the relational inductive bias of graph modeling to capture complex facial dynamics that indicate acute stress. By using Differential Action Units, the framework normalizes individual responses relative to neutral baselines to achieve personalized recognition. The proposed model achieves 88.62\% accuracy on a diverse stress-induction cohort (58 participants) using a subject-independent, Leave-One-Subject-Out (LOSO) cross-validation protocol. Beyond predictive accuracy, the architecture integrates a Multiple Instance Learning (MIL) attention mechanism to identify peak stress intervals and reveal distinct expressivity phenotypes. By simultaneously optimizing for accuracy and interpretability, this framework provides a robust, explainable solution for personalized affective monitoring.

Figures

Figures reproduced from arXiv: 2607.20819 by Giorgos Giannakakis, Nikolaos Smyrnis, Stefanos Gkikas, Thomas Kassiotis.

Figure 1
Figure 1. Figure 1: System overview of the proposed StressGAT pipeline. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Receiver Operating Characteristic (ROC) Curve for the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Clustering of participant stress phenotypes based on [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

55 extracted references · 8 linked inside Pith

  1. [1]

    Review on psychological stress detection using biosignals,

    G. Giannakakis, D. Grigoriadis, K. Giannakaki, O. Simantiraki, A. Roni- otis, and M. Tsiknakis, “Review on psychological stress detection using biosignals,”IEEE Transactions on Affective Computing, vol. 13, no. 1, pp. 440–460, 2022

    2022

  2. [2]

    Machine learning for personalised stress detection: Inter-individual variability of eeg-ecg markers for acute-stress response,

    L. Gonzalez-Carabarin, E. Castellanos-Alvarado, P. Castro-Garcia, and M. Garcia-Ramirez, “Machine learning for personalised stress detection: Inter-individual variability of eeg-ecg markers for acute-stress response,” Computer Methods and Programs in Biomedicine, vol. 209, p. 106314, 2021

    2021

  3. [3]

    Ecg-based biometric under different psychological stress states,

    R. Zhou, C. Wang, P. Zhang, X. Chen, L. Du, P. Wang, Z. Zhao, M. Du, and Z. Fang, “Ecg-based biometric under different psychological stress states,”Computer Methods and Programs in Biomedicine, vol. 202, p. 106005, 2021

    2021

  4. [4]

    Facial expression recognition: A survey of techniques, datasets, and real-world challenges,

    M. Abdeldayem, H. F. A. Hamed, and A. M. Nagy, “Facial expression recognition: A survey of techniques, datasets, and real-world challenges,” vol. 15, pp. 733–761, Oct. 2025

    2025

  5. [5]

    Facial expression and gesture recognition system for stress detection with deep learning,

    P. G. D. K. Kumarihamy, “Facial expression and gesture recognition system for stress detection with deep learning,”International Journal of Research and Scientific Innovation (IJRSI), vol. 13, no. 2, 2026

    2026

  6. [6]

    Stress and anxiety detection using facial cues from videos,

    G. Giannakakis, M. Pediaditis, D. Manousos, E. Kazantzaki, F. Chiarugi, P. Simos, K. Marias, and M. Tsiknakis, “Stress and anxiety detection using facial cues from videos,”Biomedical Signal Processing and Control, vol. 31, pp. 89–101, 2017

    2017

  7. [7]

    The effect of acute social stress on the recognition of facial expression of emotions,

    C. Daudelin-Peltier, H. Forget, C. Blais, A. Desch ˆenes, and D. Fiset, “The effect of acute social stress on the recognition of facial expression of emotions,”Scientific Reports, vol. 7, no. 1, p. 1036, Apr 2017

    2017

  8. [8]

    Automated analysis of facial cues from videos as a potential method for differentiating stress and boredom of players in games,

    F. Bevilacqua, H. Engstr ¨om, and P. Backlund, “Automated analysis of facial cues from videos as a potential method for differentiating stress and boredom of players in games,”International Journal of Computer Games Technology, vol. 2018, no. 1, p. 8734540, 2018

    2018

  9. [9]

    Static for dynamic: Towards a deeper understanding of dynamic facial expressions using static expression data,

    Y . Chen, J. Li, Y . Zhang, Z. Hu, S. Shan, M. Wang, and R. Hong, “Static for dynamic: Towards a deeper understanding of dynamic facial expressions using static expression data,”IEEE Transactions on Affective Computing, vol. 17, no. 1, p. 438–451, Jan. 2026. [Online]. Available: http://dx.doi.org/10.1109/TAFFC.2025.3623135

    arXiv 2026

  10. [10]

    Rethinking the learning paradigm for dynamic facial expression recognition,

    H. Wang, B. Li, S. Wu, S. Shen, F. Liu, S. Ding, and A. Zhou, “Rethinking the learning paradigm for dynamic facial expression recognition,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 17 958–17 968

    2023

  11. [11]

    Deep-learning-based stress recognition with spatial-temporal facial information,

    T. Jeon, H. B. Bae, Y . Lee, S. Jang, and S. Lee, “Deep-learning-based stress recognition with spatial-temporal facial information,”Sensors, vol. 21, no. 22, 2021

    2021

  12. [12]

    Facial obstruc- tions and baseline correction shape affective computing’s detection of emotion–behavior relationships,

    A. Shepelenko, V . Kosonogov, and A. N. Shestakova, “Facial obstruc- tions and baseline correction shape affective computing’s detection of emotion–behavior relationships,”Frontiers in Psychology, vol. V olume 17 - 2026, 2026

    2026

  13. [13]

    Unlocking the emotional world of visual media: An overview of the science, research, and impact of understanding emotion,

    J. Z. Wang, S. Zhao, C. Wu, R. B. Adams, M. G. Newman, T. Shafir, and R. Tsachor, “Unlocking the emotional world of visual media: An overview of the science, research, and impact of understanding emotion,” Proceedings of the IEEE, vol. 111, no. 10, p. 1236–1286, Oct. 2023

    2023

  14. [14]

    Real-time facial emotion recognition for ai-enhanced personalized learning,

    V . Tulsani, “Real-time facial emotion recognition for ai-enhanced personalized learning,”Journal of Information Systems Engineering and Management, vol. 10, pp. 427–436, 04 2025

    2025

  15. [15]

    Explainable ai in high-stakes domains: Improving trust, trans- parency, and accountability in automated decision-making,

    S. Badam, “Explainable ai in high-stakes domains: Improving trust, trans- parency, and accountability in automated decision-making,”European Journal of Computer Science and Information Technology, vol. 14, no. 2, pp. 13–34, 2026

    2026

  16. [16]

    Explainable multitask burnout prediction using adaptive deep learning (embrace) for resident physicians: Algorithm development and validation study,

    S. Alam and M. Alam, “Explainable multitask burnout prediction using adaptive deep learning (embrace) for resident physicians: Algorithm development and validation study,”JMIR AI, vol. 5, p. e57025, Jan 2026

    2026

  17. [17]

    An exploratory analysis of pain localization via explainable computational modeling,

    I. Kyprakis, S. Gkikas, E. Nichols, Y . Fang, and M. Tsiknakis, “An exploratory analysis of pain localization via explainable computational modeling,” 2026

    2026

  18. [18]

    How attentive are graph attention networks?

    S. Brody, U. Alon, and E. Yahav, “How attentive are graph attention networks?” 2022. [Online]. Available: https://arxiv.org/abs/2105.14491

    Pith/arXiv arXiv 2022

  19. [19]

    Xu and G

    D. Xu and G. Albeaino, “A systematic and bibliometric review on physiological monitoring systems and wearable sensing devices for mental status monitoring in construction: Trends, limitations, and future directions,”Journal of Information Technology in Construction (ITcon), vol. 30, pp. 1814–1865, 2025

    2025

  20. [20]

    Wearable-based stress detection for real-world data: Perspective on challenges and recommendations,

    M. Hosseini, R. Gottumukkala, R. Bhupatiraju, A. Maida, and H. Chu, “Wearable-based stress detection for real-world data: Perspective on challenges and recommendations,”JMIR Preprints, February 2026, preprint ID: 93741

    2026

  21. [21]

    Extending anxiety detection from multimodal wearables in controlled conditions to real-world environments,

    A. Alkurdi, M. He, J. Cerna, J. Clore, R. Sowers, E. T. Hsiao-Wecksler, and M. E. Hernandez, “Extending anxiety detection from multimodal wearables in controlled conditions to real-world environments,”Sensors, vol. 25, no. 4, 2025

    2025

  22. [22]

    Video-based stress detection through deep learning,

    H. Zhang, L. Feng, N. Li, Z. Jin, and L. Cao, “Video-based stress detection through deep learning,”Sensors, vol. 20, no. 19, 2020

    2020

  23. [23]

    Combining facial videos and biosignals for stress estimation during driving,

    P. Valergaki, V . C. Nicodemou, I. Oikonomidis, A. Argyros, and A. Roussos, “Combining facial videos and biosignals for stress estimation during driving,” 2026. [Online]. Available: https: //arxiv.org/abs/2601.04376

    Pith/arXiv arXiv 2026

  24. [24]

    Employing multimodal machine learning for stress detection,

    R. Walambe, P. Nayak, A. Bhardwaj, and K. Kotecha, “Employing multimodal machine learning for stress detection,”Journal of Healthcare Engineering, vol. 2021, p. 1–12, Oct. 2021

    2021

  25. [25]

    Concordance between facial micro-expressions and physiological signals under emotion elicitation,

    B. Zou, Y . Wang, X. Zhang, X. Lyu, and H. Ma, “Concordance between facial micro-expressions and physiological signals under emotion elicitation,”Pattern Recognition Letters, vol. 164, pp. 200–209, 2022

    2022

  26. [26]

    An amplification of feedback from facial muscles strengthened sympathetic activations to emotional facial cues,

    I.-S. Lee, S.-S. Yoon, S.-H. Lee, H. Lee, H.-J. Park, C. Wallraven, and Y . Chae, “An amplification of feedback from facial muscles strengthened sympathetic activations to emotional facial cues,”Autonomic Neuroscience, vol. 179, no. 1, pp. 37–42, 2013

    2013

  27. [27]

    Ekman and W

    P. Ekman and W. V . Friesen,Facial Action Coding System: A Technique for the Measurement of Facial Movement. Palo Alto, CA: Consulting Psychologists Press, 1978

    1978

  28. [28]

    Automatic stress detection evaluating models of facial action units,

    G. Giannakakis, M. R. Koujan, A. Roussos, and K. Marias, “Automatic stress detection evaluating models of facial action units,” in2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), 2020, pp. 728–733

    2020

  29. [29]

    Action unit enhance dynamic facial expression recognition,

    F. Liu, L. Gu, C. Shi, and X. Fu, “Action unit enhance dynamic facial expression recognition,” 2025. [Online]. Available: https://arxiv.org/abs/2507.07678

    Pith/arXiv arXiv 2025

  30. [30]

    A non- invasive approach for facial action unit extraction and its application in pain detection,

    M. Bouazizi, K. Feghoul, S. Wang, Y . Yin, and T. Ohtsuki, “A non- invasive approach for facial action unit extraction and its application in pain detection,”Bioengineering, vol. 12, no. 2, p. 195, 2025

    2025

  31. [31]

    Deep learning approaches for stress detection: A survey,

    M. Kyrou, I. Kompatsiaris, and P. C. Petrantonakis, “Deep learning approaches for stress detection: A survey,”IEEE Transactions on Affective Computing, vol. 16, no. 2, pp. 499–517, 2025

    2025

  32. [32]

    Real-time stress detection using facial images and convolutional neural networks,

    S. M, T. Ashwin, S. S. V , S. S, P. P, and R. Ponnusamy, “Real-time stress detection using facial images and convolutional neural networks,” in2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2024, pp. 432–437

    2024

  33. [33]

    A novel lightweight deep learning approach for drivers’ facial expression detection,

    J. Uddin, “A novel lightweight deep learning approach for drivers’ facial expression detection,”Designs, vol. 9, no. 2, 2025

    2025

  34. [34]

    Deep learning based affective computing,

    S. Kumar, “Deep learning based affective computing,”Journal of Enterprise Information Management, vol. 34, no. 5, pp. 1551–1575, 10 2021

    2021

  35. [35]

    Recurrent neural networks: A comprehensive review of architectures, variants, and applications,

    I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent neural networks: A comprehensive review of architectures, variants, and applications,” Information, vol. 15, no. 9, 2024

    2024

  36. [36]

    Transfer: Learning relation-aware facial expression representations with transformers,

    F. Xue, Q. Wang, and G. Guo, “Transfer: Learning relation-aware facial expression representations with transformers,” 2021. [Online]. Available: https://arxiv.org/abs/2108.11116

    Pith/arXiv arXiv 2021

  37. [37]

    ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment,

    S. Gkikas, Y . Fang, C. A. Cruz, M. U. Khan, and R. F. Rojas, “ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment,” in2026 14th International Conference on Affective Com- puting and Intelligent Interaction (ACII). IEEE, 2026

    2026

  38. [38]

    Engagement measurement based on facial landmarks and spatial-temporal graph convolutional networks,

    A. Abedi and S. S. Khan, “Engagement measurement based on facial landmarks and spatial-temporal graph convolutional networks,” 2024. [Online]. Available: https://arxiv.org/abs/2403.17175

    Pith/arXiv arXiv 2024

  39. [39]

    Revolutionizing facial emotion recognition: in-depth analysis of cutting-edge models, methodologies, and datasets,

    K. Sarvakar and K. Rana, “Revolutionizing facial emotion recognition: in-depth analysis of cutting-edge models, methodologies, and datasets,” Discovery Artificial Intelligence, vol. 5, no. 388, 2025

    2025

  40. [40]

    Bansal and V

    M. Bansal and V . Vyas, “An evolutionary and insightful graph neural network based hybrid model for deciphering tenacious stress detection of humans using facial emotion recognition: coinage to affective computing,” SciEnggJ, vol. 17, pp. 96–102, 03 2024

    2024

  41. [41]

    Openface 2.0: Facial behavior analysis toolkit,

    T. Baltruˇsaitis, A. Zadeh, Y . C. Lim, and L. philippe Morency, “Openface 2.0: Facial behavior analysis toolkit,”2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59–66, 2018

    2018

  42. [42]

    Assessing automated facial action unit detection systems for analyzing cross-domain facial expression databases,

    S. Namba, W. Sato, M. Osumi, and K. Shimokawa, “Assessing automated facial action unit detection systems for analyzing cross-domain facial expression databases,”Sensors, vol. 21, no. 12, 2021

    2021

  43. [43]

    Convolutional experts constrained local model for facial landmark detection,

    A. Zadeh, T. Baltru ˇsaitis, and L.-P. Morency, “Convolutional experts constrained local model for facial landmark detection,” 2017. [Online]. Available: https://arxiv.org/abs/1611.08657

    Pith/arXiv arXiv 2017

  44. [44]

    Do i have your attention: A large scale engagement prediction dataset and baselines,

    M. Singh, X. Hoque, D. Zeng, Y . Wang, K. Ikeda, and A. Dhall, “Do i have your attention: A large scale engagement prediction dataset and baselines,” 2023. [Online]. Available: https://arxiv.org/abs/2302.00431

    Pith/arXiv arXiv 2023

  45. [45]

    Daisee: Towards user engagement recognition in the wild,

    A. Gupta, A. D’Cunha, K. Awasthi, and V . Balasubramanian, “Daisee: Towards user engagement recognition in the wild,” 2022. [Online]. Available: https://arxiv.org/abs/1609.01885

    Pith/arXiv arXiv 2022

  46. [46]

    The faces of engagement: Automatic recognition of student engagement from facial expressions,

    J. Whitehill, Z. N. Serpell, Y .-C. Lin, A. Foster, and J. R. Movellan, “The faces of engagement: Automatic recognition of student engagement from facial expressions,”IEEE Transactions on Affective Computing, vol. 5, pp. 86–98, 2014

    2014

  47. [47]

    Dynamic stress detection: A study of temporal progression modelling of stress in speech,

    V . Lall and Y . Liu, “Dynamic stress detection: A study of temporal progression modelling of stress in speech,” 2025

    2025

  48. [48]

    A compar- ative study of window size and channel arrangement on eeg-emotion recognition using deep cnn,

    P. Keelawat, N. Thammasan, M. Numao, and B. Kijsirikul, “A compar- ative study of window size and channel arrangement on eeg-emotion recognition using deep cnn,”Sensors, vol. 21, no. 5, 2021

    2021

  49. [49]

    Development of a ternary levels emotion classification model utilizing electroencephalography data set,

    H. Okumu s ¸and E. Erg ¨un, “Development of a ternary levels emotion classification model utilizing electroencephalography data set,”Konya Journal of Engineering Sciences, vol. 13, no. 2, p. 607–623, 2025

    2025

  50. [50]

    Automatic stress analysis from facial videos based on deep facial action units recognition,

    G. Giannakakis, M. R. Koujan, A. Roussos, and K. Marias, “Automatic stress analysis from facial videos based on deep facial action units recognition,”Pattern analysis and applications, vol. 25, no. 3, pp. 521– 535, 2022

    2022

  51. [51]

    Automatic stress recognition using facs from prominent facial regions,

    P. Gupta, S. Maji, V . K. Jain, and S. Agarwal, “Automatic stress recognition using facs from prominent facial regions,” in2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), 2024, pp. 488–492

    2024

  52. [52]

    Studies of interference in serial verbal reactions,

    J. R. Stroop, “Studies of interference in serial verbal reactions,”Journal of Experimental Psychology, vol. 18, no. 6, pp. 643–662, 1935

    1935

  53. [53]

    A comprehensive review of the paced auditory serial addition test (pasat),

    T. N. Tombaugh, “A comprehensive review of the paced auditory serial addition test (pasat),”Archives of Clinical Neuropsychology, vol. 21, no. 1, pp. 53–76, 2006

    2006

  54. [54]

    Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment,

    S. Gkikas, C. A. Cruz, C. Joseph, G. Giannakakis, and R. F. Rojas, “Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment,” in2026 14th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2026

    2026

  55. [55]

    A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities,

    S. Gkikas, C. A. Cruz, V . Becchetti, M. U. Khan, A. Giuseppi, and R. F. Rojas, “A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities,” inProceedings of the 28th ACM International Conference on Multimodal Interaction. Association for Computing Machinery, 2026

    2026

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.