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obseed HRV Analytics

Most wearables make HRV visible almost entirely through nightly RMSSD and readiness. obseed adds real RR-interval analysis for your activities: one feature, two capabilities, clearly named.

The Problem

Your watch shows sleep HRV. What about your workout?

Most wearable apps reduce HRV to nightly RMSSD, baseline, and a readiness indicator. That is valuable for recovery, but it leaves the RR intervals from your activities mostly unused.

  • Most wearables treat nightly RMSSD as the HRV source of truth.
  • Workout HRV can show DFA alpha-1 and exercise physiology, but it cannot replace a nightly recovery baseline.
  • Sleep HRV can reveal recovery and adaptation trends, but it cannot find VT1/VT2 crossovers inside a session.

The Solution

One HRV experience. Sleep trends plus sport analysis.

obseed keeps the valuable sleep-HRV perspective and adds what wearables rarely analyze: workout HRV from real RR intervals. Activities provide session metrics, DFA alpha-1, exercise physiology, and signal quality. Nights provide RMSSD-based recovery trends, baselines, data coverage, and long-term adaptation.

What you get

Workout HRV Analysis

Activity RR intervals are analyzed across 9 domains: time-domain, frequency-domain, nonlinear, recurrence, multiscale entropy, and exercise physiology.

Sleep HRV Trends

Nightly RMSSD and ln(RMSSD) signals show recovery context, 28-day baselines, data coverage, and long-term adaptation.

Not Sleep-Only HRV

obseed makes it clear whether an insight comes from nightly RMSSD or from real activity RR intervals.

Reports With Context

Workout details, recovery trends, and quality indicators are prepared so your coach, lab, or diary sees the same context.

60+

workout HRV metrics

9

session domains

2.16%

MAPE chest strap accuracy

vs. 17.49% wrist

28 days

sleep HRV baseline

Sleep HRV context

This is how nights become a recovery trend.

Nightly RMSSD values are treated as their own data track: with baseline, coverage, missing nights, and long-term adaptation – not as a substitute for session HRV.

The difference

Same heartbeat. More than nightly RMSSD.

See what becomes visible when HRV does not stop at sleep baseline and readiness, but also analyzes your activity RR intervals.

Without HRV Analytics

  • Sleep HRV as the whole HRV story

    Your watch shows nightly RMSSD and readiness. Useful for recovery, but barely an analysis of what happens inside your activities.

  • Workout RR stays raw material

    Even when RR intervals are recorded in an activity, most wearables do not use them for DFA, threshold exploration, or session segments.

  • No quality checks

    Wrist PPG error jumps from 0.49% at rest to 26.83% while cycling (PMC, 2025). Artifacts and ectopic beats? Silently averaged into your score.

With obseed HRV Analytics

  • Workout HRV: 60+ session metrics

    RMSSD, SDNN, DFA alpha-1, Poincaré scatter, LF/HF ratio, and Sample Entropy describe your activity from RR intervals.

  • Threshold exploration only in session context

    DFA alpha-1 crossover analysis surfaces early indicators of where your VT1 and VT2 may lie. An experimental research lens on your field data.

  • Transparent quality per data track

    Nightly RMSSD and ln(RMSSD) trends stay separate from session metrics. Provider coverage, signal source, and validity tiers stay visible (chest strap: 2.16% MAPE, wrist: 6.82%).

Workout HRV visualizations

See what an activity reveals physiologically.

These 8 chart types belong to the RR-interval session analysis. They explain load, dynamics, and quality inside an activity – separate from your nightly recovery trends.

Autonomic Balance Gauge

PNS and SNS indexes in the context of the recorded session. This is workout HRV, not your nightly readiness score.

RR-Interval Histogram

Distribution of your inter-beat intervals inside an activity. Narrow or wide shows how stable the session was.

Frequency Spectrum

VLF, LF, HF power bands. Visualize the balance between sympathetic drive and parasympathetic recovery.

Multiscale Entropy

Complexity across 20 time scales. Healthy hearts are complex — rigidity signals trouble.

Recurrence Diagram

Detect hidden patterns and state transitions in your heart rhythm over time.

DFA Scaling Plot

Fractal analysis that can hint at where your ventilatory thresholds (VT1/VT2) may lie. Experimental.

Poincaré Scatter Plot

See your beat-to-beat variability in two dimensions. The shape of the scatter reveals your autonomic balance.

Windowed Trends

RMSSD, SDNN, and DFA alpha-1 tracked across your activity with VT overlay markers.

One feature for your HRV.
Two clear physiological stories.

obseed HRV Analytics doesn’t just analyze – it separates correctly. Workout HRV explains load and session dynamics. Sleep HRV explains recovery, baseline, and adaptation over time.

Read Recovery Without Session Noise

Sleep HRV

Nightly RMSSD and ln(RMSSD) trends are compared with your 28-day baseline, variability, and data coverage. Readiness stays a sleep signal, not a workout mix.

Early Hints at Your Training Zones

Workout HRV

DFA alpha-1 crossover analysis during your endurance sessions can surface early indicators of where your VT1 and VT2 thresholds may lie. An experimental research feature — not a lab replacement, but a starting point.

Attach Tags to the Right HRV Track

Context

Caffeine, sleep quality, medication, or stress can sit beside nightly trends and activities. The analysis stays clear because obseed shows whether an effect appears in sleep HRV or workout HRV.

Track Trends Across Months

Long-Term Adaptation

Sleep HRV looks at 90-, 180-, and 365-day trajectories, year-over-year deltas, and HR coupling. That describes adaptation over time, not the dynamics of one session.

Trust the Right Claim

Data Quality

Session HRV shows sensor quality, ectopic correction, and validity tiers. Sleep HRV shows provider coverage, missing nights, and baseline sufficiency.

Use Multiple Sources Deliberately

Your Wearable

Garmin, Wahoo, and Polar provide activity data when RR intervals are recorded. Nightly HRV comes from supported sleep providers and stays labeled separately.

Understand Each Workout

Session Detail

Activities and multi-session workouts are evaluated individually. See how HRV, heart rate, and load markers interact inside a session.

Share Context, Not Just Scores

Reports

Reports separate workout analysis, nightly recovery trends, and quality indicators. Professional enough for a sports science lab, accessible enough for your training diary.

How it works

Two Data Flows. One HRV View.

Step 1: Connect Your Sources

Connect activity sources like Garmin, Wahoo, or Polar and your supported sleep providers. obseed keeps workout HRV and sleep HRV separate even when they appear in one feature.

Step 2: Collect Sessions and Nights

Record activities with RR intervals and sync your nights. For session HRV, chest straps provide the cleanest RR data; wrist-based HRM is supported, but labeled accordingly as a signal source.

Step 3: Get Your Analysis

obseed detects workout RR data for the 60+ session metrics and nightly RMSSD values for recovery, baseline, and adaptation. View both tracks in the app or report.

The Kicker
Works retroactively, as always

HRV Analytics available immediately

Upload or sync your historical data. obseed analyzes past activities with RR intervals and builds nightly HRV trends from existing sleep data.

Your HRV history, sorted by source.

Not sure if your watch records RR-intervals?

Most Garmin, Wahoo, and Polar devices from the last 5 years do for activities. For sleep HRV, what matters is whether your provider delivers nightly RMSSD data.

Using a Garmin? Enable HRV recording first to make sure your data comes through.

Ready to understand workout HRV and sleep HRV clearly?