Presage Breathing Model Card


THE FOLLOWING INFORMATION, AND PRESAGE'S SDK AND APP, ARE OFFERED FOR GENERAL WELLNESS AND INFORMATIONAL PURPOSES ONLY. NEITHER HAVE BEEN CLEARED BY THE FDA AND NEITHER MAY BE USED FOR MEDICAL DIAGNOSIS OR TREATMENT.

1. Model Details

Basic info: Presage vitals by video analysis generates breathing rate metrics from a video of a subject.

Organization: Presage Technologies

Model date: 2026-09-29

Model version: fed9fe66

Model type: A proprietary computer vision and signal processing pipeline that estimates breathing rate and breathing waveform from video of a subject.

License: The algorithm is currently proprietary, and licenses are granted with predefined agreement.

Contact: Questions can be sent to: support@presagetech.com


2. Intended Use

Model Uses

This breathing model was intended for use in the analysis and non-diagnostic utility of breathing mechanics. It was intended to be used with a video from a stationary device that contains the subject's face, chest and shoulders in view. It requires approximately 45 seconds of uninterrupted data. It is only intended to measure breathing rate values in the range of 4-40 breaths per minute.

Out-of-Scope Uses

As noted above. The Presage breathing model is not intended for diagnostic purposes. No alarms are provided and it is not an apnea monitoring or detection model.


3. Validation

Reference Standard: ETCO2 capnography via Biopac MP160. For each SDK breathing-rate timestamp t, the CO2 reference breathing rate is computed from CO2 peaks detected in a 30-second lookback window ending at t:

BRCO2 = 60 · (N − 1) / (tlast peak − tfirst peak)
where N is the number of CO2 peaks in the window, tlast peak is the time of the last peak, and tfirst peak is the time of the first peak. This produces a continuous ground-truth breathing rate aligned to each SDK output timestamp, rather than a simple count of peaks per window.

Comparison Methodology: Breathing measurements from the camera-based system were compared against time-aligned reference measurements. Ground truth signals were checked for quality using labeled signal annotations; segments with poor signal quality were excluded from analysis.


4. Data Demographics

Category Distribution
Total 93 subjects, 184 videos
Camera (videos) Samsung S24 Rear 24mm (tripod): 92, Logitech C920: 92
Sex (subjects) Female: 50, Male: 43
Age Group (subjects) 18-25: 29, 26-35: 39, 36-45: 12, 46-55: 6, 56-65: 6, 65+: 1
Fitzpatrick (subjects) Type 1: 14, Type 2: 14, Type 3: 7, Type 4: 24, Type 5: 21, Type 6: 13
Lighting (videos) Ring Light: 184

Reference standard: ETCO2 via biopac peak detection for breathing rate ground truth.


5. Data Provenance

Reference Instrumentation: Biopac research-grade physiological sensors: ETCO2 capnography (for breathing rate ground truth).

Camera Devices Tested: Samsung S24 Rear 24mm (tripod), Logitech C920 (tripod).

Average Camera Distance: Logitech C920: 36", Samsung S24 Rear 24mm: 39"

Data Handling: All subject data is de-identified. Derived metrics and anonymized identifiers are retained. Data is securely stored with access restricted to trained researchers.


6. Factors

The breathing metric model requires face and pose detection to identify the subject's chest region for motion analysis.


These factors can affect model performance:


Lighting Conditions Tested:


Other factors:


7. Metrics

(at 80% Return Rate, Confidence >= 79)

  1. MAE: 0.37 BrPM
  2. RMSE: 0.67 BrPM

8. Quantitative Analysis

Computed vs Ground Truth at Confidence Thresholds

Bland-Altman Plot

Confidence Lookup Table

Confidence >= MAE (BrPM) RMSE (BrPM) Pearson r Return Rate (%) N (samples)
0 0.70 1.88 0.886 100.0 4113
10 0.70 1.88 0.886 100.0 4113
20 0.69 1.83 0.890 99.9 4110
30 0.66 1.69 0.904 99.5 4092
40 0.60 1.48 0.925 98.3 4044
50 0.49 0.96 0.967 95.3 3919
60 0.46 0.85 0.974 93.4 3840
65 0.44 0.79 0.977 91.7 3770
70 0.42 0.74 0.980 89.1 3666
75 0.40 0.72 0.981 85.3 3508
79 0.37 0.67 0.984 80.0 3300
80 0.37 0.66 0.984 78.3 3221
85 0.30 0.58 0.987 60.3 2482
90 0.20 0.38 0.993 11.4 469

Performance

(at 80% Return Rate, Confidence >= 79)

By Camera Type

Camera Type N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Samsung S24 Rear 24mm (tripod) 1605 78.5 0.37 0.65 0.984
Logitech C920 1695 82.0 0.38 0.68 0.983

By Fitzpatrick Skin Type

Fitzpatrick N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Type I 409 71.4 0.42 0.66 0.987
Type II 447 75.0 0.33 0.51 0.990
Type III 254 84.7 0.34 0.50 0.982
Type IV 840 83.9 0.29 0.51 0.992
Type V 853 84.9 0.34 0.60 0.988
Type VI 497 77.9 0.60 1.08 0.913

Note: All Fitzpatrick types were tested under Ring Light only.

By Sex

Sex N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Male 1475 76.4 0.43 0.78 0.979
Female 1825 83.6 0.33 0.56 0.988

By Age Group

Age Group N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
18-25 925 72.1 0.42 0.70 0.983
26-35 1365 84.4 0.38 0.72 0.981
36-45 553 89.9 0.31 0.49 0.984
46-55 226 71.3 0.39 0.71 0.986
56-65 211 81.8 0.29 0.52 0.991
65+ 20 87.0 0.17 0.22 0.969

By Lighting Type

Lighting N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Ring Light 3300 80.2 0.37 0.67 0.984

Confidence vs Breathing Waveform Correlation

Confidence >= Upper Waveform Pearson r Lower Waveform Pearson r Return Rate (%) N (videos)
0 0.794 0.623 100.0 184
10 0.794 0.623 100.0 184
20 0.794 0.623 100.0 184
30 0.794 0.623 100.0 184
40 0.802 0.624 98.9 182
50 0.809 0.625 97.3 179
60 0.827 0.646 94.0 173
65 0.839 0.653 91.3 168
70 0.844 0.659 88.0 162
75 0.851 0.664 82.6 152
80 0.857 0.655 73.4 135
85 0.880 0.648 48.9 90
90 0.934 0.684 1.6 3

Waveform Example


9. Fairness & Equity

Bias Assessment Methodology: Performance is stratified by Fitzpatrick skin type (I-VI), sex, camera type, and age group. Per-group metrics and Confidence averages are reported in the Quantitative Analysis tables above.


10. Ethical Considerations

As a remote sensing device, the risks posed to the subjects in the trial are minimal, including the association of each subject with corresponding biometric data. Mitigation of these risks include de-identifying all subject data, including videos, prior to saving it. Additionally, all data is securely stored with access to a select number of trained researchers.

The model is not intended for human life-critical decisions, diagnostics or prognostication.

11. Limitations and Tradeoffs