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REVIEW 4 major objections 5 minor 32 references

Passive Measurement of Autonomic Arousal in Real-World Settings

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Using only wrist-worn sensors, the Fitbit Body Response Algorithm detects acute autonomic arousal and predicts perceived stress in daily life with 85% accuracy, significantly above chance.

desk verdict A substantial product-grade stress-sensing pipeline, but the free-living validation is undermined by labels generated from the same physiology the model consumes; the headline balanced accuracy of 0.69 should not be taken at face value. read the letter →

arxiv 2504.21242 v1 pith:4WQPZZQ5 submitted 2025-04-30 cs.HC cs.LG

Samy Abdel-Ghaffar , Isaac Galatzer-Levy , Conor Heneghan , Xin Liu , Sarah Kernasovskiy , Brennan Garrett , Andrew Barakat , Daniel McDuff This is my paper · ORCID
classification cs.HCcs.LG
keywords wearablehealthmonitoringsystemsdevicespsychologicalstressbiosensorsautonomicnervoussystemelectrodermalactivityheartratevariabilityecologicalmomentaryassessment
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

The paper seeks to establish that acute autonomic nervous system arousal, the body's stress response, can be measured continuously and passively from wrist-worn consumer sensors in everyday settings, not just in the laboratory. To do this it introduces the Fitbit Body Response Algorithm, which combines heart rate, heart rate variability, electrodermal activity, and skin temperature into ensemble logistic-regression models, and validates the approach in a controlled stress-induction study and a week-long free-living study. In free-living conditions the algorithm predicts perceived stress with accuracy 0.85, balanced accuracy 0.69, sensitivity 0.47, and specificity 0.91, all significantly above chance by permutation test. The authors argue that each sensor is a fallible indicator of the latent stress construct, and that combining them, together with filters for exercise, water exposure, and loose device wear, yields a stable real-world measure that lab-only methods cannot provide.

What carries the argument

The central object is the Fitbit Body Response Algorithm, a signal-processing and classification pipeline for wrist-worn devices. It takes 14 minutely input signals, consisting of nine heart-rate-variability metrics, mean heart rate, electrodermal slope and magnitude, and skin-temperature slope and magnitude, z-scores them per user, forms 31-minute sliding windows, and extracts time-series features from each window. Univariate selection keeps the top 20 features per signal group, and three nested logistic-regression models are trained leave-one-subject-out on the Trier Social Stress Test data: electrodermal activity plus temperature, adding heart rate, and adding heart rate variability. At run time the largest model with available sensors is selected, so the ensemble covers 100% of minutes that any model can cover. Confounder filters remove minutes with exercise, water exposure, and loose device wear before classification, and post-processing discards stress predictions shorter than 3 minutes and stitches events closer than 5 minutes. The mechanism's job is to separate true autonomic events from the noise and artifacts that dominate real-world wear.

What would settle it

A controlled TSST replication in which stress minutes are defined by the protocol's fixed phases or by an independent biomarker such as salivary cortisol, without the annotators seeing the wrist signals, would settle whether the model detects arousal rather than the annotators' visual labels; if balanced accuracy falls to the permutation null, circular labeling is the source of the signal. Likewise, a free-living validation using only randomly timed EMA prompts would test whether the reported 0.69 balanced accuracy survives unbiased sampling.

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Extended reading notes

Core claim

The central claim is that autonomic arousal can be indexed in real-world conditions from wrist sensors alone, and that a modular ensemble of physiological signals does this better than any single modality. The paper reports that a logistic-regression model trained on manually labeled acute-arousal minutes from a virtual Trier Social Stress Test learns the dynamics of arousal onset, including rising heart rate and electrodermal activity and falling heart rate variability, rather than mere baseline shifts between stress and recovery periods. In one week of free living, the full Body Response Algorithm detected perceived-stress events with accuracy 0.85, balanced accuracy 0.69, sensitivity 0.47, specificity 0.91, and F1 0.47; every metric exceeded the permutation-test null at p<0.001. Adding cardiovascular signals to electrodermal activity and skin temperature increased sensitivity from 0.25 to 0.47 at the cost of specificity falling from 0.97 to 0.91, which the authors present as consistent with their latent-variable premise that sympathetic and parasympathetic indicators are weak signals that become more stable when combined.

Load-bearing premise

The load-bearing premise is that labels created by visually inspecting the same physiological signals the model sees, and by self-reports triggered by those signals, actually mark true autonomic arousal rather than encoding the algorithm's own assumptions.

Editorial extensions

If this is right

  • A consumer smartwatch can passively flag likely stress episodes throughout the day without requiring the user to answer questions or interact with the device.
  • Combining electrodermal activity and skin temperature with cardiovascular signals raises sensitivity for detecting arousal, so multi-sensor ensembles should be preferred over single-signal stress detectors.
  • Algorithmic availability can be kept near 100% by running the largest model whose inputs are currently valid, which matters because noisy or missing heart rate variability is common in real life, occurring in 38.9% of minutes in this dataset.
  • Real-world confounders such as exercise, water exposure, and loose device wear can be handled with explicit filters before classification, which is required for acceptable specificity in free-living use.
  • Detected stress events can trigger just-in-time interventions such as mood logging, guided breathing, or mindfulness prompts.

Reading between the lines

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

  • Because the TSST labels were created by visually inspecting the same physiological traces used as model inputs, the criterion-validation numbers may partly reflect the authors' labeling conventions; a replication with labels fixed by protocol phase or by an independent marker such as cortisol would test whether the algorithm detects arousal rather than the annotators' judgments.
  • The free-living labels were not sampled uniformly: notifications were triggered by an HR/HRV-based detector and by nightly EDA-event surveys, so the validation set oversamples times the sensors already thought were stressful; a study with purely random EMA prompts would give a cleaner estimate of real-world sensitivity and specificity.
  • The same availability-aware nested-model design could generalize to other mobile-health sensing problems in which sensors intermittently fail or are intentionally turned off, such as seizure detection or sleep staging.
  • Because physiological arousal also accompanies positive excitement, a natural extension is to test whether the algorithm can distinguish negative stress from positive arousal or to add subjective valence labels; the paper's discussion of positive appraisals leaves this as an open problem.
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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 proposes the Fitbit Body Response Algorithm, an ensemble of logistic-regression models that classify minutes of autonomic arousal from wrist-worn EDA, skin temperature, HR, and HRV signals. The pipeline includes preprocessing, per-user z-score normalization, tsfresh-derived features with univariate selection, confounder filtering (exercise, water exposure, loose wear), and post-processing that discards short predictions and stitches nearby events. Validation is attempted in two settings: a virtual Trier Social Stress Test (TSST) with n=45, using manually created stress/no-stress labels, and a one-week free-living ecological momentary assessment (EMA) study with n=87, using stress-log notifications and retrospective EDA surveys as labels. The paper reports TSST balanced accuracy of 0.73 for the full logistic-regression model and free-living accuracy of 0.85, balanced accuracy of 0.69, sensitivity of 0.47, and specificity of 0.91 for the combined Body Response Algorithm, with permutation-test p-values below 0.001.

Significance. If the validation were sound, this would be a significant contribution: it describes a deployed consumer-wearable stress-detection algorithm with a transparent signal-processing chain, explicit handling of real-world confounders, a four-classifier comparison, and a relatively large free-living EMA dataset. The authors are candid about label noise, the arbitrary ±10-minute tolerance, and the limitations of virtual TSST delivery. However, both validation arms are compromised by label construction that depends on the same physiological signals the model uses as features. As a result, the reported effect sizes cannot currently be interpreted as evidence for accurate measurement of autonomic arousal or perceived stress; the contribution at this stage is primarily an engineering description rather than a validated measurement method.

major comments (4)
  1. [Section II.B.1 and Section IV.C] The TSST ground-truth labels are circular. Section II.B.1 states that "minutes which showed changes in the physiological signals commensurate with an autonomic arousal event were labeled stress," and these signals are exactly the EDA, HR, HRV, and skin-temperature inputs used by the model (Section III.A). The LOSO metrics in Tables 3–5 therefore measure how well the model reproduces the annotators' visual pattern recognition, not how well it detects an independent criterion of autonomic arousal. The statement in Section IV.C that the manual labels were not cherry-picked does not address this issue: manual annotation still requires an external reference such as traditional TSST period labels, cortisol or subjective ratings, or pre-registered event criteria with inter-rater reliability. Please report the traditional-label model as the primary TSST validation and treat the manually labeled model as descriptive rather than as evidence of criterion validity.
  2. [Section II.B.2, II.B.3, and IV.D] The free-living labels are generated from the same physiological signals the model consumes. Stress-log notifications are triggered by a previously created HR/HRV-based stress detection algorithm (Section II.B.2), and retrospective EDA surveys are automatically generated for detected EDA events, with stress/no-stress labels derived from the user's attribution of those events (Section II.B.3). Because EDA, HR, and HRV are model features, positive stress labels are preferentially located at moments when these signals are elevated. A classifier can obtain high performance by learning the trigger conditions of the labeling process rather than by learning to measure autonomic arousal or perceived stress. Moreover, the no-stress labels from the EDA surveys are minutes with EDA events attributed to Heat/Exertion, Humidity, or Unknown, so the contrast is between user-attributed causes of EDA events, not between arousal and non-arousal. Please re-analyze using only the randomly timed notifications that the protocol already includes, or explicitly model the label-generation process.
  3. [Section III.C and IV.D] The event-matching rule and the permutation test do not remove the label-selection bias. Section III.C states that a detected stress event within ±10 minutes of an actual stress event is considered correct, and that if one or more predicted stress events overlap an actual event, the entire actual event is marked correct while the predicted events are discarded. This lenient scoring can inflate accuracy, sensitivity, and specificity. The permutation test shuffles detected stress responses in time within subject-days, but the null distribution inherits the same sampling preference for signal elevations, so a significant p-value only shows that temporal alignment with labels is better than random; it does not demonstrate that predictions capture stress rather than the trigger characteristics of the labeling process. Please report metrics with no tolerance, with strict minute-level matching, and without the event-discard rule, and provide a null model that simulates labels generated by the same triggering process.
  4. [Section III.B and IV.C] Threshold selection and feature selection may leak information into the reported performance. The production probability threshold of 0.72 is described as selected "through extensive testing of subject preferences," but the specificity of 0.90 reported in Table 4 is computed on the TSST training dataset, which suggests the threshold was chosen on the same data used for evaluation. In addition, the univariate feature selection with Benjamini-Hochberg correction (Section III.A) appears to be applied to the entire TSST dataset before the LOSO cross-validation loop; if so, the LOSO performance estimates in Tables 3–5 are optimistic. Please clarify the exact cross-validation nesting and, if necessary, perform feature selection and threshold selection inside each training fold.
minor comments (5)
  1. [Table 2] The function name "energey_ratio_by_chunks" appears to be a typo for "energy_ratio_by_chunks."
  2. [Figure 1] The figure shows only mean time series relative to stress onsets; adding confidence intervals or per-subject traces would be needed to support the claim of large inter-individual variability.
  3. [Abstract and Table 6] The headline accuracy of 0.85 in the abstract is driven by class imbalance and the lenient matching rule; please report balanced accuracy, sensitivity, and specificity alongside accuracy in the abstract.
  4. [Section II.A.2.i] The number and timing of EMAs and STAI administrations is described inconsistently ("first and second EMAs" versus a "3rd stress EMA" after recovery); please clarify the exact timeline of survey administration.
  5. [General] For reproducibility, please state whether the data and analysis code are available or will be made available, given the study's corporate funding and the description of a proprietary algorithm.

Circularity Check

2 steps flagged · score 8.0 of 10

TSST ground-truth labels are defined by visual inspection of the same HR/HRV/EDA/temperature signals used as model features, and free-living EMA labels are triggered by HR/HRV and EDA detectors, so both validations reduce partly to reproducing the physiology that generates the labels.

  1. self definitional [Section II.B.1 (Stress Labels - TSST)]
    "Because the primary aim of this study was to build an algorithm that can identify the onset of autonomic arousal events, and not to conduct a traditional TSST analysis, we manually created stress/no-stress labels that did not follow the traditional labeling approach. Minutes which showed changes in the physiological signals commensurate with an autonomic arousal event were labeled stress, and all other minutes labeled no-stress, independent of which of the 4 TSST periods they fell in."

    The model's inputs are 14 minutely signals from HR, HRV, EDA, and skin temperature (Section III.A). The TSST stress labels are defined by the experimenters visually identifying changes in those same physiological signal families. Training and LOSO evaluation on these labels therefore measure how well the classifier reproduces the annotators' visual reading of the input signals, not agreement with an independent autonomic-arousal criterion. The paper's claim that the TSST provides criterion validity reduces by construction to self-agreement with the label-generation process.

  2. other [Sections II.B.2, II.B.3, IV.D]
    "A previously created HR/HRV-based stress detection algorithm was used to detect potential moments of physiological stress response. If the HR/HRV-based algorithm detected physiological stress, and it had been 45 minutes since the last notification was sent, then a new notification was sent. This script identified EDA events where the EDA signal showed a meaningful increase in SCL value."

    Free-living positive stress labels are not randomly sampled perceived-stress ratings: stress-log notifications are fired by an HR/HRV detector, and retrospective surveys are auto-generated for detected EDA events. The Body Response model consumes the same signal families (HR, HRV, EDA, skin temperature). Thus positive labels are preferentially located at moments where HR/HRV or EDA are already elevated, and no-stress labels from EDA surveys are also EDA-event minutes attributed to Heat/Exertion/Humidity/Unknown. The model can score well by learning the trigger conditions of the labeling process rather than by independently measuring perceived stress; the permutation test shuffles detected events in time but does not remove this selection bias.

full rationale

The central validation chain is circular at its two anchor points. In the TSST, the 'gold standard' stress labels were manually created by inspecting the identical physiological signals that become the feature set, so the reported TSST metrics (ROC AUC 0.79, balanced accuracy 0.73 at threshold 0.5) quantify reproduction of human annotation, not independent criterion validity. In free living, EMA stress labels are event-triggered by HR/HRV-based detection and EDA event detection, placing positive labels at signal elevations from the same modalities the model uses; this biases the headline free-living balanced accuracy of 0.69 upward. The paper is not entirely circular because the free-living EMA responses are subjective self-reports and include randomly timed no-stress probes, and because the algorithm is a real signal-processing and modeling engineering effort. However, the two validations offered as evidence for 'continuous remote measurement of ANS activation' both reduce in part to the physiology used to create the labels, so a score of 8 is warranted. No load-bearing self-citation chain or uniqueness argument was found; the circularity is in the label-generation design, not in the references.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central validation rests on labels that are either derived from the input signals (TSST manual labels) or sampled by physiology-triggered notifications (free-living EMA and EDA surveys), plus several threshold choices that influence reported performance.

free parameters (7)
  • Production probability threshold = 0.72
    Selected through 'extensive testing of subject preferences' (Section IV.C) to reduce false positives; all reported free-living and TSST metrics depend on this threshold.
  • Event matching tolerance = +/- 10 minutes
    Free-living predictions within 10 minutes of a stress EMA are counted as correct (Section III.C), a lenient scoring rule the authors call 'somewhat arbitrary'.
  • EMA stress binarization cutoff = 1-2 no-stress, 3-5 stress
    Stress logs on a 1-5 Likert scale are dichotomized at 3 (Section II.B.2).
  • Exercise detection probability thresholds = 80% (HR), 90% (EDA)
    Minutes are excluded when an unpublished logistic regression classifier exceeds these probabilities (Section II.C.5).
  • Loose wear filter thresholds = 50% open-circuit EDA + 5 minutes
    Minutes with at least half the minute at open-circuit EDA, plus 5 minutes afterward, are removed (Section II.C.5).
  • Label duration from retrospective EDA surveys = EDA event duration
    Stress/no-stress labels for free-living EDA events span the detected event duration, which is determined by the same EDA signal used as a model input (Section II.B.3).
  • TSST manual label criteria = Visual inspection of physiological signals
    Stress/no-stress minutes were manually labeled based on changes in the same physiological signals used as model features (Section II.B.1), effectively fitting the target to the inputs.
assumptions (5)
  • domain assumption Stress is a latent construct measurable as autonomic arousal, and each physiological signal is a fallible indicator of it.
    Invoked in the Introduction via latent variable measurement theory [9] to justify combining weak signals.
  • ad hoc to paper Manually identified physiological changes in TSST recordings correspond to true autonomic arousal events.
    The TSST ground truth is defined by the authors' visual inspection of the input signals (Section II.B.1), an assumption that is not externally validated.
  • domain assumption A virtual, video-conferencing TSST with pre-recorded panels induces stress comparable to the in-person TSST.
    The protocol was adapted due to COVID-19 and relies on reference [15] for the validity of remote TSST versions.
  • domain assumption Perceived stress self-reports (EMA) are a valid proxy for autonomic arousal events in free-living conditions.
    The paper acknowledges the relationship between perceived and physiological stress is modest [8], yet EMA labels are used as ground truth.
  • domain assumption EDA survey responses of 'Heat/Exertion', 'Humidity', or 'Unknown' indicate non-stress arousal and serve as valid no-stress labels.
    These labels are treated as ground truth in free-living evaluation (Section II.B.3), but an 'Unknown' response does not establish absence of stress.

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Cite this review

Pith. "Pith review of Passive Measurement of Autonomic Arousal in Real-World Settings." pith.science (2026). https://pith.science/paper/4WQPZZQ5

@misc{pith2026250421242,
  author       = {Pith},
  title        = {Pith review of: Passive Measurement of Autonomic Arousal in Real-World Settings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4WQPZZQ5}},
  note         = {Machine review of arXiv:2504.21242}
}
read the original abstract

The autonomic nervous system (ANS) is activated during stress, which can have negative effects on cardiovascular health, sleep, the immune system, and mental health. While there are ways to quantify ANS activity in laboratories, there is a paucity of methods that have been validated in real-world contexts. We present the Fitbit Body Response Algorithm, an approach to continuous remote measurement of ANS activation through widely available remote wrist-based sensors. The design was validated via two experiments, a Trier Social Stress Test (n = 45) and ecological momentary assessments (EMA) of perceived stress (n=87), providing both controlled and ecologically valid test data. Model performance predicting perceived stress when using all available sensor modalities was consistent with expectations (accuracy=0.85) and outperformed models with access to only a subset of the signals. We discuss and address challenges to sensing that arise in real world settings that do not present in conventional lab environments.

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Works this paper leans on

32 extracted references · 22 canonical work pages

  1. [3]

    DOI: 10.1016/S0165-0327(00)00338-4

    doi:10.1016/s0165-0327(00)00338-4

  2. [5]

    Stress and the individual. Mechanisms leading to disease,

    B. S. McEwen and E. Stellar, “Stress and the individual. Mechanisms leading to disease,” Arch. Intern. Med., vol. 153, no. 18, pp. 2093–2101, Sep. 27

    work page

  3. [8]

    DOI: 10.1016/j.biopsycho.2010.01.017

    work page doi:10.1016/j.biopsycho.2010.01.017 2010

  4. [10]

    DOI: 10.1109/TBME.2009.2038487

    arXiv 2009

  5. [12]

    Call Center Stress Recognition with Person-Specific Models,

    J. Hernandez, R. R. Morris, and R. W. Picard, “Call Center Stress Recognition with Person-Specific Models,” in Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, 2011, pp. 125–134. DOI: 10.1007/978-3-642-24600-5_16

    doi:10.1007/978-3-642-24600-5_16 2011

  6. [13]

    DOI: 10.1016/j.ijpsycho.2014.09.011

    doi:10.1016/j.ijpsycho.2014.09.011 2014

  7. [23]

    DOI: 10.1037/a0024278

    work page doi:10.1037/a0024278

  8. [24]

    DOI: 10.1016/j.cpr.2010.03.005

    work page doi:10.1016/j.cpr.2010.03.005 2010

Show all 32 references
  1. [26]

    DOI: 10.1155/2021/5924040 13 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <

    2021 doi

  2. [28]

    Effectiveness of progressive muscle relaxation, deep breathing, and guided imagery in promoting psychological and physiological states of relaxation,

    L. Toussaint, Q. A. Nguyen, C. Roettger, K. Dixon, M. Offenbächer, N. Kohls, et al., “Effectiveness of progressive muscle relaxation, deep breathing, and guided imagery in promoting psychological and physiological states of relaxation,” Evid. Based Complement. Alternat. Med., ...

    2021

  3. [29]

    Guidelines for Assessing and Minimizing Risks of Emotion Recognition Applications

    “Guidelines for Assessing and Minimizing Risks of Emotion Recognition Applications.” In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), 1–8. IEEE. DOI: 10.1109/ACII52823.2021.9597452

    2021

  4. [1968]

    His research aims are to build technologies and do basic science that improve the mental and physical health of people

    DOI: 10.1111/j.1469-8986.1968.tb02783.x Samy Abdel-Ghaffar , is a Senior Research Scientist at Google. His research aims are to build technologies and do basic science that improve the mental and physical health of people. He received his PhD at the University of California, B...

    1968

  5. [1988]

    DOI: 10.1111/j.1469-8986.1988.tb01899.x

    1988

  6. [1992]

    DOI: 10.1016/0022-3999(92)90072-A

    doi

  7. [1993]

    DOI: 10.1001/archinte.1993.00410180039004

    1993

  8. [2000]

    DOI: 10.1146/annurev.neuro.23.1.155

    doi

  9. [2001]

    Conor Heneghan (Senior Member, IEEE) is a Senior Staff Research Scientist at Google

    Isaac Galatzer-Levy , photograph and biography not available at the time of publication. Conor Heneghan (Senior Member, IEEE) is a Senior Staff Research Scientist at Google. He completed his PhD at Columbia University, NY in 1995, and also holds a BEng from University College D...

    1990

  10. [2004]

    DOI: 10.1088/0967-3334/25/5/020

    doi

  11. [2005]

    DOI: 10.1109/TITS.2005.848368

    2005

  12. [2007]

    DOI: 10.1152/physrev.00041.2006

    2006

  13. [2010]

    DOI: 10.1016/j.neubiorev.2009.10.002

    2009 doi

  14. [2011]

    DOI: 10.1007/s12671-011-0043-8

    doi

  15. [2012]

    DOI: 10.1016/j.psyneuen.2011.12.010

    2011 doi

  16. [2013]

    Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations

    “Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations.” In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 600–605. IEEE. DOI: 10.1109/ACII.2013.105

    2013 doi

  17. [2014]

    DOI: 10.1016/j.neubiorev.2013.11.005

    2013 doi

  18. [2015]

    DOI: 10.1016/j.jpsychores.2015.03.009

    2015 doi

  19. [2016]

    DOI: 10.1186/s13030-016-0059-3

    doi

  20. [2017]

    DOI: 10.3389/fpubh.2017.00258

    2017

  21. [2018]

    Brennan Garrett , photograph and biography not available at the time of publication

    Sarah Kernasovskiy , photograph and biography not available at the time of publication. Brennan Garrett , photograph and biography not available at the time of publication. Andrew Barakat , photograph and biography not available at the time of publication. Daniel McDuff (Membe...

    2014

  22. [2020]

    DOI: 10.1016/S2589-7500(20)30246-6

    doi

  23. [2021]

    DOI: 10.1016/j.ijpsycho.2021.03.009

    2021 doi

  24. [2024]

    What Does Large-Scale Electrodermal Sensing Reveal?

    “What Does Large-Scale Electrodermal Sensing Reveal?” bioRxiv. https://doi.org/DOI: 10.1101/2024.02.22.581472

    2024 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.