Pith. sign in

REVIEW 4 major objections 5 minor 40 references

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read A model trained on one three-month period loses about 5% accuracy when applied to a later period, showing that the physiology-to-arousal relationship shifts over time within individuals.

desk verdict A genuinely new longitudinal dataset and a plausible drift signal, but the headline 5% drop is statistically under-supported; worth refereeing. read the letter →

arxiv 2508.18782 v1 pith:VKGMRJ6H submitted 2025-08-26 cs.HC cs.AI

Hiroto Sakimura , Takayuki Nagaya , Tomoki Nishi , Tetsuo Kurahashi , Katsunori Kohda , Nobuhiko Muramoto This is my paper · ORCID
classification cs.HCcs.AI
keywords emotionestimationphysiologicalsignalstemporalvariabilityarousalwearablesensorsexplainableboostingmachineselectrodermalactivitylongitudinalstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the relationship between physiological signals and self-reported arousal is not stable over months-long periods within the same person. From 24 office workers (six analyzed in depth) wearing wrist sensors for two three-month periods, the authors find that a model trained on the first period drops about 5% in accuracy when tested on the second. The interpretable model they use shows the shift is not uniform across features: heart rate stays a reliable predictor, while a minimum-skin-conductance feature shifts the most. If correct, this undermines a common static assumption in affective computing and argues for periodic retraining or drift-triggered updates in real-world emotion-estimation systems.

What carries the argument

The central object is the Explainable Boosting Machine (EBM) with a period-interaction decomposition: the log-odds of high arousal are a sum of period-invariant feature functions plus a second-period-specific shift function (scaled by a period dummy). This lets the authors separate common physiology-arousal curves from time-specific deviations per feature. A second quantitative tool is Pearson's correlation coefficient r between the common and total second-period feature functions, used as a per-feature, per-participant stability score.

What would settle it

Measure the same individuals in a third period that falls in the same season as the first period (e.g., the following winter). If a model trained on the first period performs as well on this third period as on first-period test data, then the observed drop is seasonal, not long-term drift.

Watch

Extended reading notes

Core claim

Using Explainable Boosting Machines with a period-interaction term, the authors estimate each physiological feature's contribution to binary high/low arousal separately for a common effect and a second-period-specific shift. Across six participants with enough data in both periods, training on the first period and testing on the second lowered accuracy from 0.771 to 0.716 (AUC from 0.586 to 0.548), while training and testing within the second period scored 0.731, showing the drop is not simply because the second period is harder. The additive-function analysis singles out minimum EDA as the least temporally stable feature and heart rate as the most stable. The paper interprets these results

Load-bearing premise

The only time comparison is between two three-month blocks in different seasons, so the 5% drop could come from seasonal or environmental changes rather than a genuine long-term shift in how physiology maps to arousal.

Editorial extensions

If this is right

  • Emotion-estimation systems deployed for months should expect accuracy decay; retraining on recent data (the paper suggests about every five months) can recover part of the lost performance.
  • Heart rate is a comparatively dependable predictor across time, so models can keep relying on it; the electrodermal minimum is a drift-prone feature that may need recalibration.
  • Monitoring performance or feature-contribution shifts can serve as an adaptive trigger for updating, instead of a fixed schedule.
  • Combining first- and second-period training data improved second-period prediction over first-period-only training, indicating that including recent data mitigates drift.
  • The r-based stability score offers a simple way to flag which physiological features have shifted for which individuals.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the two collection windows are in different seasons (winter and early summer), the 5% drop and EDA_min instability could be explained by ambient temperature, humidity, or daylight rather than by a slow within-person drift; a third same-season measurement would separate these.
  • If environmental drivers dominate, the 'five-month update' rule is a proxy; a more direct implication is that models should carry season or environmental context as features, which the paper notes but does not test.
  • A stronger test of the drift claim would be to fit the same EBM on monthly or biweekly windows and see whether feature functions change continuously or jump at a seasonal boundary; the paper's two-point design cannot distinguish these.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper investigates whether the mapping between wearable physiological features and self-reported arousal is stable within individuals over several months. Data were collected from 24 office workers over two three-month periods (November 2023–February 2024 and April–July 2024); a subset of six participants with sufficient high/low arousal labels was used for within-subject EBM modeling. The main claim is that a model trained on the first period loses about 5% accuracy (0.771 to 0.716; AUC 0.586 to 0.548, Table II) when tested on the second period, indicating long-term temporal variability in physiological–arousal associations. Using the EBM decomposition in Eq. (1), the authors further report that HR is the most stable predictor while EDA_min is the most variable, and they recommend periodic model updating approximately every five months. The paper explicitly lists several limitations: small participant count, two periods only, potential seasonal confounds, and a label distribution biased toward low-arousal states.

Significance. If the central claim is statistically supported, the paper would provide useful empirical evidence that within-individual physiological–arousal relationships are not stationary over months, with practical implications for adaptive emotion-estimation systems. The study's strengths include a longitudinal naturalistic dataset, a transparent interpretable model (EBM), and explicit acknowledgment of many limitations. However, the headline result rests on only six participants and on comparisons that lack between-participant statistical inference, and the design cannot separate temporal drift from seasonal/environmental changes. The feature-stability analysis also has a self-referential validation component. These issues are fixable in revision, but they currently limit the strength of the abstract and conclusion claims.

major comments (4)
  1. [Section IV-B, Table II] The principal evidence for temporal shift is the 0.055 accuracy drop and 0.038 AUC drop, but the reported standard errors are computed from 100 repeated train/test splits within each participant and then averaged across the six participants. This reflects split-sampling noise, not participant-level variability. No paired test, confidence interval, or participant-level effect size is reported. With n=6, the average drop could be driven by one or two participants. In addition, the AUC values (0.548–0.586) are close to chance, so the classifier is only weakly related to arousal. Please report per-participant accuracies/AUCs and conduct a between-participant statistical test (e.g., paired bootstrap or mixed-effects model) before claiming a reliable cross-period drop.
  2. [Section III-A and Section V] The two collection periods are winter (Nov–Feb) and early summer (Apr–Jul), so period is completely confounded with season, temperature, humidity, daylight, and clothing. The authors acknowledge in Section V that 'it remains unclear whether observed patterns follow circadian, seasonal, or irregular trends.' Nevertheless, the Abstract and Conclusion attribute the drop to 'long-term variability' and recommend a five-month update cycle. This is stronger than the evidence supports. To make the temporal-drift claim, the authors should either include environmental covariates, compare same-season periods, or explicitly reframe the conclusion as evidence of cross-period instability without attributing it to a specific temporal mechanism.
  3. [Section IV-C, Figures 3–5] The temporal-shift analysis using Pearson's r is partly circular. In Eq. (1), fcom and fint are fitted on the same data, and r is computed between fcom and fcom+fint. The paper then validates r 'aligning well with qualitative observations' by visually inspecting those same fitted functions. This does not provide independent evidence that the physiological–arousal relationship has changed; it mostly reflects the model's own parameterization and estimation noise. Please provide a null distribution (e.g., permutation or block bootstrap) for r, or evaluate period-specific models on held-out data, before ranking features by temporal stability.
  4. [Section III-C and Section IV-B] Feature selection is performed on the combined dataset across all participants and both data collection periods, before the train/test splits used in Table II. This means that 2nd-period data (including the participants later used as test subjects) is used to select the five features that are then evaluated in the cross-period test. This can leak information and biases the reported performance. Please use nested feature selection inside the training periods, or at least demonstrate that the selected features are stable across periods when selection is performed on the 1st period only.
minor comments (5)
  1. [Section IV-C, Fig. 4 caption] The caption says 'fcom reflects the common contribution of HR' but the figure is about EDA_min. This appears to be a copy-paste error and should be corrected.
  2. [Section III-D / IV-B] The description 'randomly sampled 90 data from both the 1st and 2nd periods' is ambiguous. Was the sampling stratified by arousal class? Given that the dataset is imbalanced toward low-arousal states, the class distribution of the 90 samples per period affects accuracy and AUC. Please report the class balance and consider class-balanced evaluation.
  3. [Table II caption] The phrase 'for the six participants per each' is grammatically unclear. Also, the caption should make explicit that the standard errors are within-participant split-sampling errors averaged over participants, not between-participant errors.
  4. [Section V] The recommendation to update 'every five months' is presented in the Abstract and Conclusion, but Section V itself states that 'rather than adhering to a fixed update cycle, a more practical approach may involve monitoring model performance.' Please align the wording so the five-month figure is not presented as a validated constant.
  5. [Figures 3–5] The x- and y-axis labels are missing or too small. The y-axis of the additive function plots should indicate log-odds contribution, and the x-axis should indicate feature values. This is needed to interpret the shape and shift of the functions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central train/test drop is an out-of-sample comparison, and the r-based feature analyses are descriptive rather than predictions.

full rationale

The paper's central empirical claim—a 5% accuracy drop when a model trained on 1st-period data is tested on 2nd-period data (Table II, case (b) vs (a))—is an ordinary out-of-sample evaluation and does not reduce by construction to any fitted parameter. The EBM equation (Eq. 1) includes a period-specific term, but the observed drop is measured on held-out 2nd-period data, so it is not a renaming of the model's inputs. The subsequent r-based stability analysis (Section IV-C) is descriptive: r is computed from the fitted fcom and fcom+fint functions, and the paper uses visual inspection to check that r reflects shape changes. This is a weak internal consistency check, not a circular derivation of a predicted quantity. There are no substantive self-citations: the method citation [40] is to an external InterpretML framework, and no load-bearing argument invokes prior work by the same authors. The acknowledged limitations—small n=6, winter/summer confound, weak arousal range—undermine the causal interpretation as 'long-term variability' and the five-month update recommendation, but those are correctness/external-validity risks rather than circularity under the defined patterns. Feature selection on the combined dataset is a potential leakage concern, but it would not force the observed drop and is not a definitional equivalence.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

This is an empirical measurement paper, not a derivation. The central claim rests on the quality and representativeness of the self-reported arousal labels, the timing of physiological windows, and the assumption that the two-period design isolates temporal drift from seasonality. No new entities are postulated and no new physical mechanisms are introduced.

free parameters (4)
  • EBM additive functions (fcom_i, fint_i)
    The temporal-shift analysis is built on these functions fitted per participant on the same data used to evaluate the shift, so they are learned quantities, not independent measurements.
  • 3-sigma outlier threshold
    Hand-chosen threshold for removing outliers per participant and feature (Section III-B); affects the feature distributions feeding the models.
  • 90 samples per period per participant
    Hand-chosen sample size for repeated training (Section III-D); the paper states 90 samples per period, giving 18 samples per feature.
  • 50-second stationary window
    Hand-chosen window length for pairing physiological data with emotion annotations (Section III-A), which defines the dataset and thus all downstream results.
assumptions (4)
  • domain assumption Binary arousal derived from Core Affect categories faithfully represents the subjective arousal dimension.
    Every emotion label is mapped to high or low arousal based on its position in the Core Affect space (Section III-C); the validity of the target variable depends on this mapping.
  • domain assumption The two three-month periods are sufficient to assess long-term temporal variability rather than seasonal confounds.
    The central claim of long-term variability assumes the observed shift is not primarily due to the winter-to-summer season change; the paper itself flags this as unresolved (Section V).
  • domain assumption Physiological signals in the stationary 50-second window before annotation reflect the annotated emotional state.
    The annotation system triggers only after 50 seconds of stationary posture, and the preceding 50-second window is paired with the emotion report (Section III-A).
  • domain assumption EBM as a GAM approximation adequately captures the physiology-arousal relationship.
    All conclusions about feature stability and temporal shift rest on the assumption that the additive structure of Eq. 1 is a reasonable model of the true relationship (Section III-D).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation." pith.science (2026). https://pith.science/paper/VKGMRJ6H

@misc{pith2026250818782,
  author       = {Pith},
  title        = {Pith review of: Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VKGMRJ6H}},
  note         = {Machine review of arXiv:2508.18782}
}
read the original abstract

Estimating emotional states from physiological signals is a central topic in affective computing and psychophysiology. While many emotion estimation systems implicitly assume a stable relationship between physiological features and subjective affect, this assumption has rarely been tested over long timeframes. This study investigates whether such relationships remain consistent across several months within individuals. We developed a custom measurement system and constructed a longitudinal dataset by collecting physiological signals -- including blood volume pulse, electrodermal activity (EDA), skin temperature, and acceleration--along with self-reported emotional states from 24 participants over two three-month periods. Data were collected in naturalistic working environments, allowing analysis of the relationship between physiological features and subjective arousal in everyday contexts. We examined how physiological-arousal relationships evolve over time by using Explainable Boosting Machines (EBMs) to ensure model interpretability. A model trained on 1st-period data showed a 5\% decrease in accuracy when tested on 2nd-period data, indicating long-term variability in physiological-arousal associations. EBM-based comparisons further revealed that while heart rate remained a relatively stable predictor, minimum EDA exhibited substantial individual-level fluctuations between periods. While the number of participants is limited, these findings highlight the need to account for temporal variability in physiological-arousal relationships and suggest that emotion estimation models should be periodically updated -- e.g., every five months -- based on observed shift trends to maintain robust performance over time.

Figures

Figures reproduced from arXiv: 2508.18782 by the authors.

Figure 1
Figure 1. (a) The core affect model as selections of subjective emotion [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Data amount for each participant in the 1st and 2nd period [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Relations between HR and its contribution to predict arousal [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Relations between EDA_min and its contribution to pre [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Cross-period variability characterized by correlation co [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 38 canonical work pages

  1. [1]

    R. W. Picard,Affective Computing. MIT Press, 2000

    2000

  2. [2]

    Approaches,applications, and challenges in physiological emotion recognition—a tutorial overview,

    Y.S.Can,B.Mahesh,andE.André,“Approaches,applications, and challenges in physiological emotion recognition—a tutorial overview,” Proceedings of the IEEE, vol. 111, no. 10, pp. 1287– 1313, 2023

    work page 2023

  3. [3]

    Feature extraction and selection for emotion recog- nition from electrodermal activity,

    J. Shukla, M. Barreda-Angeles, J. Oliver, G. C. Nandi, and D. Puig, “Feature extraction and selection for emotion recog- nition from electrodermal activity,”IEEE Transactions on Af- fective Computing, vol. 12, no. 4, pp. 857–869, 2019

    work page 2019

  4. [4]

    Heart rate variability as an index of regulated emotional responding,

    B. M. Appelhans and L. J. Luecken, “Heart rate variability as an index of regulated emotional responding,”Review of general psychology, vol. 10, no. 3, pp. 229–240, 2006

    work page 2006

  5. [5]

    Emotion recognition from physiological signal analysis: A review,

    M. Egger, M. Ley, and S. Hanke, “Emotion recognition from physiological signal analysis: A review,” Electronic Notes in Theoretical Computer Science, vol. 343, pp. 35–55, 2019

    work page 2019

  6. [6]

    Detecting stress during real- world driving tasks using physiological sensors,

    J. A. Healey and R. W. Picard, “Detecting stress during real- world driving tasks using physiological sensors,”IEEE Trans- actions on Intelligent Transportation Systems, vol. 6, no. 2, pp. 156–166, 2005

    work page 2005

  7. [7]

    Emotion recognition using physiological signals: laboratory vs. wearable sensors,

    M. Ragot, N. Martin, S. Em, N. Pallamin, and J.-M. Diverrez, “Emotion recognition using physiological signals: laboratory vs. wearable sensors,” inAdvances in Human Factors in Wearable Technologies and Game Design: Proceedings of the AHFE 2017 International Conference on Advances in Human Factors and Wearable Technologies, July 17-21, 2017, The Westin Bon...

    work page 2017

  8. [8]

    Reliability of the empatica e4 wristband to measure electrodermal activ- ity to emotional stimuli,

    A. Borrego, J. Latorre, M. Alcañiz, and R. Llorens, “Reliability of the empatica e4 wristband to measure electrodermal activ- ity to emotional stimuli,” in2019 international conference on virtual rehabilitation (ICVR). IEEE, 2019, pp. 1–2

    work page 2019

Show all 40 references
  1. [9]

    Comparative study of physiological signals from empatica e4 wristband for stress classification,

    V. Chandra, A. Priyarup, and D. Sethia, “Comparative study of physiological signals from empatica e4 wristband for stress classification,” in Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April23–24,2021,RevisedSelectedPaper...

    2021

  2. [10]

    Emotionsense: Emotion recognition based on wearable wristband,

    B. Zhao, Z. Wang, Z. Yu, and B. Guo, “Emotionsense: Emotion recognition based on wearable wristband,” in 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People...

    2018

  3. [11]

    Emotion recognition for everyday life using physiological signals from wearables: A systematic literature review,

    S. Saganowski, B. Perz, A. G. Polak, and P. Kazienko, “Emotion recognition for everyday life using physiological signals from wearables: A systematic literature review,”IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 1876–1897, 2022

    2022

  4. [12]

    Short- term analysis of heart rate variability for emotion recognition via a wearable ecg device,

    H. W. Guo, Y. S. Huang, J. C. Chien, and J. S. Shieh, “Short- term analysis of heart rate variability for emotion recognition via a wearable ecg device,” in 2015 International conference on intelligent informatics and biomedical sciences (ICIIBMS). IEEE, 2015, pp. 262–265

    2015

  5. [13]

    Emognition Wearable Dataset 2020,

    S. Saganowski, J. Komoszyńska, M. Behnke, B. Perz, L.D. Kaczmarek, and P. Kazienko, “Emognition Wearable Dataset 2020,” 2021. [Online]. Available: https://doi.org/10.7910/DVN/R9WAF4

    2020 doi

  6. [14]

    A dataset of continuous affect annotations and physiological signals for emotion analysis,

    K. Sharma, C. Castellini, E. L. Van Den Broek, A. Albu- Schaeffer, and F. Schwenker, “A dataset of continuous affect annotations and physiological signals for emotion analysis,” Scientific data, vol. 6, no. 1, p. 196, 2019

    2019

  7. [15]

    Emotional ratings and skin conductance response to visual, auditory and haptic stimuli,

    E. Gatti, E. Calzolari, E. Maggioni, and M. Obrist, “Emotional ratings and skin conductance response to visual, auditory and haptic stimuli,”Scientific data, vol. 5, no. 1, pp. 1–12, 2018

    2018

  8. [16]

    Autonomic nervous system activity during positive emotions: A meta-analytic review,

    M. Behnke, S. D. Kreibig, L. D. Kaczmarek, M. Assink, and J. J. Gross, “Autonomic nervous system activity during positive emotions: A meta-analytic review,” Emotion Review, vol. 14, no. 2, pp. 132–160, 2022

    2022

  9. [17]

    Emotion recognition based on physiolog- ical changes in music listening,

    J. Kim and E. André, “Emotion recognition based on physiolog- ical changes in music listening,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2067– 2083, 2008

  10. [18]

    A real-world dataset of group emotion experiences based on physiological data,

    P. Bota, J. Brito, A. Fred, P. Cesar, and H. Silva, “A real-world dataset of group emotion experiences based on physiological data,” Scientific Data, vol. 11, no. 1, p. 116, 2024

    2024

  11. [19]

    Affect detection from wear- ables in the “real

    S. K. D’Mello and B. M. Booth, “Affect detection from wear- ables in the “real” wild: Fact, fantasy, or somewhere in be- tween?” IEEE Intelligent Systems, vol. 38, no. 1, pp. 76–84, 2023

    2023

  12. [20]

    Positiveaffectandheartratevariability:Adynamic analysis,

    T. Beatton, H. F. Chan, U. Dulleck, A. Ristl, M. Schaffner, and B.Torgler,“Positiveaffectandheartratevariability:Adynamic analysis,” Scientific Reports, vol. 14, p. 7004, 2024

    2024

  13. [21]

    Examining the role of emotion regulation in the bidirectional relation between physiological and subjective stress response among daily cigarette smokers,

    A. M. Borges, M.-J. Yang, S. G. Farris, M. Zvolensky, and T. M. Leyro, “Examining the role of emotion regulation in the bidirectional relation between physiological and subjective stress response among daily cigarette smokers,”Personality and Individual Differences, vol. 155, ...

    2020

  14. [22]

    Is there consistency and specificity of autonomic changes during emotional episodes? guidancefromtheconceptualacttheoryandpsychophysiology,

    K. S. Quigley and L. F. Barrett, “Is there consistency and specificity of autonomic changes during emotional episodes? guidancefromtheconceptualacttheoryandpsychophysiology,” Biological psychology, vol. 98, pp. 82–94, 2014

    2014

  15. [23]

    Measuring affective state: Subject-dependent and -independent prediction based on lon- gitudinal multimodal sensing,

    L. Berkemeier, W. Kamphuis, A.-M. Brouwer, H. de Vries, M. Schadd, J. U. van Baardewijk, H. Oldenhuis, R. Verdaas- donk, and L. van Gemert-Pijnen, “Measuring affective state: Subject-dependent and -independent prediction based on lon- gitudinal multimodal sensing,”IEEE Transac...

    2025

  16. [24]

    Emotional processing and the autonomic nervous system: a comprehensive meta-analytic investigation,

    P. S. Moreira, P. Chaves, N. Dias, P. Costa, and P. R. Almeida, “Emotional processing and the autonomic nervous system: a comprehensive meta-analytic investigation,” 2018

    2018

  17. [25]

    The pupil as a measure of emotional arousal and autonomic activation,

    M. M. Bradley, L. Miccoli, M. A. Escrig, and P. J. Lang, “The pupil as a measure of emotional arousal and autonomic activation,”Psychophysiology, vol. 45, no. 4, pp. 602–607, 2008

    2008

  18. [26]

    Core affect, prototypical emo- tional episodes, and other things called emotion: dissecting the elephant

    J. A. Russell and L. F. Barrett, “Core affect, prototypical emo- tional episodes, and other things called emotion: dissecting the elephant.”Journal of personality and social psychology, vol. 76, no. 5, p. 805, 1999

    1999

  19. [27]

    Inferring psychological significance from physiological signals,

    J. T. Cacioppo and L. G. Tassinary, “Inferring psychological significance from physiological signals,”American Psychologist, vol. 45, no. 1, pp. 16–28, 1990

    1990

  20. [28]

    Toward machine emotional intelligence: Analysis of affective physiological state,

    R. W. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence: Analysis of affective physiological state,” IEEEtransactionsonpatternanalysisandmachineintelligence , vol. 23, no. 10, pp. 1175–1191, 2001

    2001

  21. [29]

    A survey on databases for multimodal emotion recognition and an introduction to the viri (visible and infrared image) database,

    M. F. H. Siddiqui, P. Dhakal, X. Yang, and A. Y. Javaid, “A survey on databases for multimodal emotion recognition and an introduction to the viri (visible and infrared image) database,” Multimodal Technologies and Interaction, vol. 6, no. 6, p. 47, 2022

    2022

  22. [30]

    Low vagal tone is associated with impaired post-stress recovery of cardiovascular, endocrine, and immune markers,

    C. S. Weber, J. F. Thayer, M. Rudat, P. H. Wirtz, F. Zimmermann-Viehoff, A. Thomas, and H.-C. Deter, “Low vagal tone is associated with impaired post-stress recovery of cardiovascular, endocrine, and immune markers,”European JournalofAppliedPhysiology ,vol.109,no.2,pp.201–211,2010

    2010

  23. [31]

    Out of the lab and into the fray: Towards model- ing emotion in everyday life,

    J. Healey, L. Nachman, S. Subramanian, J. Shahabdeen, and M. Morris, “Out of the lab and into the fray: Towards model- ing emotion in everyday life,” inInternational Conference on Pervasive Computing. Springer, 2010, pp. 156–173

    2010

  24. [32]

    Healthyoffice: Mood recognition at work using smartphones and wearable sensors,

    A. Zenonos, A. Khan, G. Kalogridis, S. Vatsikas, T. Lewis, and M. Sooriyabandara, “Healthyoffice: Mood recognition at work using smartphones and wearable sensors,” in2016 IEEE inter- national conference on pervasive computing and communication workshops (PerCom Workshops). IEE...

    2016

  25. [33]

    A wearable system for mood assessment considering smartphone features and data from mobile ecgs,

    A. Exler, A. Schankin, C. Klebsattel, and M. Beigl, “A wearable system for mood assessment considering smartphone features and data from mobile ecgs,” inProceedings of the 2016 ACM international joint conference on pervasive and ubiquitous com- puting: Adjunct, 2016, pp. 1153–1161

    2016

  26. [34]

    Biraffe2, a multimodal dataset for emotion-based personalization in rich affective game environments,

    K. Kutt, D. Drążyk, L. Żuchowska, M. Szelążek, S. Bobek, and G. J. Nalepa, “Biraffe2, a multimodal dataset for emotion-based personalization in rich affective game environments,”Scientific Data, vol. 9, no. 1, p. 274, 2022

    2022

  27. [35]

    The affective computing approach to affect measurement,

    S. D’Mello, A. Kappas, and J. Gratch, “The affective computing approach to affect measurement,” Emotion Review, vol. 10, no. 2, pp. 174–183, 2018

    2018

  28. [36]

    Autonomic nervous system activity in emotion: A review,

    S. D. Kreibig, “Autonomic nervous system activity in emotion: A review,” Biological psychology, vol. 84, no. 3, pp. 394–421, 2010

    2010

  29. [37]

    A new method of assessing cardiac autonomic function and its compar- ison with spectral analysis and coefficient of variation of R–R interval,

    M. Toichi, T. Sugiura, T. Murai, and A. Sengoku, “A new method of assessing cardiac autonomic function and its compar- ison with spectral analysis and coefficient of variation of R–R interval,”Journal of the autonomic nervous system, vol. 62, no. 1-2, pp. 79–84, 1997

    1997

  30. [38]

    Scikit-learn: Machine learning in python,

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research, vol. 12, pp. 2825– 2830, 2011

    2011

  31. [39]

    Feature extraction and selec- tion for emotion recognition from EEG,

    R. Jenke, A. Peer, and M. Buss, “Feature extraction and selec- tion for emotion recognition from EEG,”IEEE Transactions on Affective computing, vol. 5, no. 3, pp. 327–339, 2014

    2014

  32. [40]

    Interpretml: A unified framework for machine learning interpretability,

    H. Nori, S. Jenkins, P. Koch, and R. Caruana, “Interpretml: A unified framework for machine learning interpretability,”arXiv preprint arXiv:1909.09223, 2019

    1909 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.