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: 2026-05-29

Model version: v3.1.0-rc.5

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, 184 videos
Camera (videos) Samsung S24 Rear 24mm (tripod): 92, Logitech C920: 92
Sex (subjects) Female: 50, Male: 43
Age Group (subjects) 18-25: 31, 26-35: 38, 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: 184

Reference standard: ETCO2 via biopac template 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 >= 45)

  1. MAE: 0.62 BrPM
  2. RMSE: 1.46 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 1.07 3.14 0.725 100.0 1531
10 0.84 2.00 0.861 96.1 1472
20 0.77 1.86 0.879 91.5 1401
30 0.71 1.75 0.892 87.3 1336
40 0.67 1.71 0.897 83.6 1280
45 0.62 1.46 0.924 80.0 1226
50 0.62 1.46 0.923 79.9 1223
60 0.55 1.18 0.951 71.5 1094
65 0.53 1.01 0.964 68.7 1052
70 0.52 1.02 0.964 65.3 1000
75 0.51 1.00 0.964 62.2 953
80 0.51 1.01 0.964 60.3 923
85 0.48 0.96 0.967 57.6 882
90 0.48 0.95 0.969 54.5 835
95 0.48 0.95 0.968 54.2 830

Performance

(at 80% Return Rate, Confidence >= 45)

By Camera Type

Camera Type N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Samsung S24 Rear 24mm (tripod) 635 82.8 0.50 0.93 0.970
Logitech C920 591 77.4 0.74 1.87 0.878

By Fitzpatrick Skin Type

Fitzpatrick N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Type I 166 76.1 0.64 1.03 0.971
Type II 162 79.8 0.57 0.85 0.975
Type III 83 76.1 1.38 2.57 0.702
Type IV 306 82.7 0.41 0.61 0.991
Type V 327 86.7 0.64 2.12 0.828
Type VI 182 71.7 0.60 1.01 0.911

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

By Sex

Sex N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Male 568 76.0 0.79 1.99 0.875
Female 658 83.9 0.47 0.74 0.977

By Age Group

Age Group N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
18-25 413 75.9 0.57 0.87 0.968
26-35 459 80.2 0.76 2.11 0.870
36-45 162 89.0 0.40 0.65 0.973
46-55 113 85.6 0.75 1.21 0.956
56-65 73 76.8 0.26 0.39 0.994
65+ 6 100.0 0.45 0.48 0.914

By Lighting Type

Lighting N (samples) Return Rate (%) MAE (BrPM) RMSE (BrPM) Pearson r
Ring Light 1226 80.1 0.62 1.46 0.924

Confidence vs Breathing Waveform Correlation

Confidence >= Upper Waveform Pearson r Lower Waveform Pearson r Return Rate (%) N (videos)
0 0.227 0.140 100.0 184
10 0.231 0.140 97.8 180
20 0.235 0.142 94.0 173
30 0.233 0.143 88.0 162
40 0.232 0.147 81.5 150
50 0.243 0.154 76.6 141
60 0.254 0.155 71.2 131
65 0.254 0.150 65.8 121
70 0.235 0.138 62.0 114
75 0.223 0.132 58.7 108
80 0.229 0.138 56.0 103
85 0.246 0.160 52.2 96
90 0.259 0.160 49.5 91
95 0.259 0.159 41.8 77

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