Pre-release

This release is a pre-release and may not be stable for production use.

SeetaPsych Hertz

Heart rate estimation modules for SeetaPsych

Usage

This project is already included in the seetapsych-lib default configuration. Download and use it via seetapsych-manager download.

For usage, refer to SeetaPsych.

You can additionally add this algorithm module using the following methods.

Heart rate estimation requires processing video or real-time video streams to extract heart rate information.

WebUI

Run seetapsych-webui with the --files argument to use it.

seetapsych-webui --files seetapsych_hertz/modules/seeta.yml

Programmatic Usage

Add the following code in your program to use this algorithm module.

from seetapsych_lib.runtime.factory import Factory
from seetapsych_lib.runtime.pipeline import Pipeline

factory = Factory()
factory.load_file_modules("seetapsych_hertz/modules/seeta.yml")

pipeline = Pipeline(factory, ...)

pipeline.add_attributes("face/heart_rate")

Module Catalog

Module YAML Path	Package Name
`seetapsych_hertz/modules/ada-chrom.yml`	HeartRate-AdaChrom
`seetapsych_hertz/modules/seeta.yml`	HeartRate-Seeta
`seetapsych_hertz/modules/tiny-hr.yml`	HeartRate-TinyHR

AdaChrom

Model-free rPPG heart rate estimation using adaptive chrominance analysis on skin ROI.

Module config: ada-chrom.yml

Package	Provides	Requires
HeartRate-AdaChrom	`face/heart_rate`	`face/dense_landmarks`

Description

Adaptive chrominance rPPG heart rate estimator from adaptive forehead ROI, no neural model required.

Parameters

Name	Type	Default	Description & Tuning
`window_samples`	integer	`300`	Sliding window frame count for HR estimation. Larger values reduce noise but increase latency; adjust based on real-time demand.

Models

(None)

SeetaHeartRateDetector

Traditional signal-processing heart rate estimation from forehead skin pixels via chrominance + FFT analysis.

Module config: seeta.yml

Package	Provides	Requires
HeartRate-Seeta	`face/heart_rate`	`face/dense_landmarks`

Description

Signal-processing heart rate estimator using forehead ROI from dense landmarks with chrominance + FFT peak detection.

Parameters

Name	Type	Default	Description & Tuning
`min_seconds`	number	`1`	Minimum buffered signal duration (seconds) before producing first HR estimate. Lower for faster startup, higher for stability.
`min_frames`	integer	`10`	Minimum frame count before HR estimation starts. Takes effect whichever is reached later between min_seconds and min_frames.
`max_frames`	integer	`300`	Maximum frames kept in the sliding analysis window. Larger windows smooth outliers but increase response lag to true HR changes.

Models

(None)

TinyHR

Lightweight neural network for fast heart rate estimation directly from face video frames.

Module config: tiny-hr.yml

Package	Provides	Requires
HeartRate-TinyHR	`face/heart_rate`	`face/detection`

Description

Fast RhythmFormer heart rate estimator using buffered face crops + Welch spectral analysis.

Parameters

Name	Type	Default	Description & Tuning
`fps`	number	`30`	Expected camera/video FPS used for spectral analysis windowing. Mismatch with actual source FPS degrades HR accuracy.
`interval`	number	`1`	Seconds between consecutive HR estimates. Smaller intervals yield more updates with higher jitter; larger intervals are smoother but slower.

Models

Model	Recommended
`seeta-hertz-tinyhr.onnx`	✓

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