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: 2026-08-06

Model version: dc437d54

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 peaktfirst 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, 182 videos
Camera (videos) Logitech C920: 92, Samsung S24 Rear 24mm (tripod): 90
Sex (subjects) Female: 50, Male: 43
Age Group (subjects) 18-25: 29, 26-35: 40, 36-45: 11, 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: 182

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: 0.38 BrPM
  2. RMSE: 0.69 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 1.92 0.879 100.0 4068
10 0.70 1.92 0.879 100.0 4068
20 0.70 1.92 0.879 100.0 4068
30 0.68 1.83 0.889 99.6 4050
40 0.61 1.51 0.921 98.3 3997
50 0.51 1.05 0.960 96.0 3905
60 0.47 0.91 0.970 94.1 3827
65 0.46 0.87 0.973 92.4 3759
70 0.43 0.78 0.978 90.0 3663
75 0.41 0.72 0.981 85.8 3491
79 0.38 0.69 0.982 80.0 3266
80 0.37 0.68 0.983 78.0 3173
85 0.30 0.58 0.987 59.5 2422
90 0.18 0.31 0.995 10.7 434

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) 1601 79.7 0.39 0.70 0.982
Logitech C920 1665 80.9 0.38 0.69 0.982

By Fitzpatrick Skin Type

Fitzpatrick N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Type I 400 71.3 0.44 0.72 0.983
Type II 446 74.3 0.34 0.50 0.991
Type III 250 83.9 0.30 0.40 0.988
Type IV 816 82.3 0.30 0.51 0.992
Type V 862 85.9 0.36 0.68 0.984
Type VI 492 80.1 0.60 1.10 0.909

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

By Sex

Sex N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Male 1478 77.4 0.43 0.79 0.978
Female 1788 82.9 0.35 0.61 0.986

By Age Group

Age Group N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
18-25 946 73.4 0.43 0.71 0.981
26-35 1418 84.4 0.37 0.70 0.981
36-45 442 87.4 0.31 0.51 0.984
46-55 232 74.1 0.52 1.02 0.975
56-65 207 80.9 0.28 0.52 0.990
65+ 21 91.3 0.16 0.21 0.969

By Lighting Type

Lighting N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Ring Light 3266 80.3 0.38 0.69 0.982

Confidence vs Breathing Waveform Correlation

Confidence >= Upper Waveform Pearson r Lower Waveform Pearson r Return Rate (%) N (videos)
0 0.799 0.615 100.0 182
10 0.799 0.615 100.0 182
20 0.799 0.615 100.0 182
30 0.799 0.615 100.0 182
40 0.802 0.616 99.5 181
50 0.809 0.616 98.4 179
60 0.812 0.616 97.3 177
65 0.835 0.630 93.4 170
70 0.840 0.629 90.1 164
75 0.847 0.649 84.1 153
80 0.857 0.634 70.9 129
85 0.876 0.620 49.5 90

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