Stress and activity
Extend autonomic analysis beyond the heart — add electrodermal activity (the sympathetic "stress" axis) and accelerometer-derived movement, and use motion to judge when ECG/HRV can be trusted.
Research question
What do skin-conductance responses and physical activity add to the autonomic picture, and how much of the HRV signal is contaminated by movement?
Who this is for
- Stress, recovery, sleep, and naturalistic-physiology researchers
- Wearable / digital health teams combining heart, electrodermal, and motion sensors
- Anyone needing motion-aware quality control on wearable ECG/HRV
Data requirements
| Data | Required | Purpose |
|---|---|---|
| EDA signal (CSV) | For EDA | Tonic/phasic decomposition and SCRs |
| Accelerometer (CSV: time + x/y/z or magnitude) | For activity / motion | ENMO activity and motion burden |
| ECG waveform | For motion quality | Pairing R-peaks against activity |
Workflow
Upload EDA / ACC → Analyze EDA, Analyze activity → (ECG) Preprocess → Motion quality → Interpret
Step 1 — Electrodermal activity
Upload an EDA recording (choose EDA as the waveform modality) and run Analyze EDA. You get:
- Skin conductance level (tonic, SCL)
- Phasic skin conductance responses — count, rate, and amplitude
- A clean-vs-tonic trace
Step 2 — Activity (accelerometer)
Upload an accelerometer CSV (choose ACC) and run Analyze activity: ENMO intensity, active vs sedentary burden, and an activity-over-time chart.
Step 3 — Motion quality (cross-modal)
On an ECG dataset, open Motion quality, pick a completed ACC run to pair, and click Check motion. The pipeline reports motion burden %, the % of beats recorded during movement, and the high-motion segments — so HRV and morphology can be caveated or filtered.
Step 4 — Interpret (optional)
Interpretation cites EDA, activity, and motion-quality metrics as Electrodermal, Activity / motion, and Motion quality modalities; high motion burden automatically adds an HRV-reliability caveat.
Expected outputs
- EDA tonic/phasic metrics and SCRs
- Activity intensity (ENMO) and sedentary/active burden
- Motion burden and beats-in-motion for a paired ECG
- Combined autonomic + motion context for interpretation
Example insight
Skin conductance responses cluster during the high-activity segment, and 24% of ECG beats fall in motion windows there — HRV from that segment should be interpreted with caution.