Longitudinal study where 10 adults completed standardized psychology tests across three weekly sessions while wearing multiple biometric sensors. Combines self-report psychometric data with real-time physiological recordings.
| Parameter | Detail |
|---|---|
| Participants | 10 adults |
| Sessions | 3 per participant (weekly intervals) |
| Design | Longitudinal, within-subjects |
| Key finding | People differed a lot from each other, but each person's pattern stayed consistent across sessions |
- HADS — Hospital Anxiety and Depression Scale
- STAI-S — State-Trait Anxiety Inventory (State subscale)
- BFI-10 — Big Five Inventory (10-item short form)
- Fear Questionnaire — Marks-Mathews phobia assessment
| Modality | Sensor | What it measures |
|---|---|---|
| Eye tracking | Pupil Labs Core | Gaze position, pupil dilation, fixations, saccades |
| Cardiac | Polar H10+ | Heart rate, HRV (SDNN, RMSSD), inter-beat intervals |
| Electrodermal | TEA GSR | Galvanic skin response, skin conductance level |
| Facial analysis | OpenFace | Action units, head pose, gaze direction |
| Hardware and sensors | Setup |
|---|---|
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| Participant in session | Session in progress |
|---|---|
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- Recruitment — Adult participants screened and enrolled
- Baseline — Resting-state sensor calibration before each session
- Assessment — Psychometric tests administered while all sensors record simultaneously
- Data collection — Synchronized multimodal streams captured per participant per session
- Analysis — Individual and group-level correlations between self-report and physiological data
How stable are these patterns within a person across sessions? A test–retest reliability check (ICC(1), n = 10, 3 sessions) on the committed summaries gives ICC = 0.22 for HRV SDNN, 0.45 for pupil-dilation variability, and 0.61 for response-duration variability — poor for the physiological measures, moderate at best. So the data do not support a strong "stable individual traits" reading: with only 10 participants, these measures look closer to session-to-session fluctuation than to reliable traits. Any stability claim should be read as tentative and underpowered.
| Standard deviation of HRV (SDNN) | Standard deviation of pupil dilation |
|---|---|
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| K-Means clusters in PCA space | Optimal cluster selection |
|---|---|
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Python · Jupyter · pandas · NumPy · SciPy · Matplotlib · Seaborn · scikit-learn
IoT · Machine Learning · Multimodal · Neurophysiological · Multi-Sensors · Psychometrics
- Sensor — Review of the biometric sensors used here
- Psychometric — Web app for the psychometric tests used in this study
- CalmSense — ML/DL stress detection from physiological signals