webcam-pulse-detector

A python application that detects the heart-rate of an individual using their computer's webcam. Tested on OSX 10.7 (Lion), Ubuntu 13.04 (Ringtail), and Windows 7.

Inspired by reviewing recent work on Eulerian Video Magnification (http://people.csail.mit.edu/mrub/vidmag/), with motivation to implement something visually comparable (though not necessarily identical in formulation) to their pulse detection examples in python-opencv. Comparable to a few previous efforts (such as https://github.com/mossblaser/HeartMonitor).

Data processing is implemented within an openMDAO (http://openmdao.org/) assembly object to facilitate rapid prototyping/redesign of the real-time analysis, and for simple embedding into a python application.

How it works:

This application uses openCV (http://opencv.org/) to find the location of the user's face, then isolate the forehead region. Data is collected from this location over time to estimate the user's heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage's green channel alone (a better color mixing ratio may exist, but the blue channel tends to be very noisy). Physiological data can be estimated this way thanks to the optical absorbtion characteristics of (oxy-) hemoglobin (see http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-16-26-21434).

With good lighting and minimal noise due to motion, a stable heartbeat should be isolated in about 15 seconds. Other physiological waveforms, such as Mayer waves (http://en.wikipedia.org/wiki/Mayer_waves), should also be visible in the raw data stream.

Once the user's pulse signal has been isolated, real-time phase variation associated with the detected hearbeat frequency is also computed. This allows for the heartbeat frequency to be exaggerated in the post-process frame rendering; causing the highlighted forhead location to pulse in sync with the user's own heartbeat.

Support for pulse-detection on multiple simultaneous people in an camera's image stream is definitely possible, but at the moment only the information from one face is extracted for analysis

The overall dataflow/execution order for the real-time signal processing looks like:

This signal processing design is implemented in the openMDAO assembly object defined in lib/processors.py.

The definition of each component block used can be found in the source files lib/imageProcess.py, lib/signalProcess.py, and lib/sliceops.py. The @bin and @bout blocks in the above graph denote assembly-level input and output.

Requirements:

• Python v2.7+ (http://python.org/)
• OpenCV v2.4+, with the cv2 python bindings (http://opencv.org/)

OpenCV is a powerful open-source computer vision library, with a convenient numpy-compatible interface in the cv2 bindings.

• OpenMDAO v0.5.5+ (http://openmdao.org/)

OpenMDAO is an open-source engineering framework that serves as a convenient enviroment to containerize the required real-time analyses, and allow for it to be easily tweaked to specification. It requires python 2.6+, numpy, scipy, and matplotlib (see http://openmdao.org/docs/getting-started/requirements.html)

Quickstart:

• Activate the openMDAO virtual python environment in a command or terminal window

. OpenMDAO/bin/activate

• In the activated environment, navigate to the downloaded source directory, and run get_pulse.py to start the application

python get_pulse.py

• If there is an error, try running test_webcam.py in the same directory to check if your openCV installation and webcam can be made to work with this application.

Usage notes:

• When run, a window will open showing a stream from your computer's webcam
• When a forehead location has been isolated, the user should press "S" on their keyboard to lock this location, and remain as still as possible (the camera stream window must have focus for the click to register). This freezes the aquisition location in place. This lock can be released by pressing "S" again.
• To view a stream of the measured data as it is gathered, press "D". To hide this display, press "D" again.
• The data display shows three data traces, from top to bottom:
  1. raw optical intensity
  2. extracted heartbeat signal
  3. Power spectral density, with local maxima indicating the heartrate (in beats per minute).
• With consistent lighting and minimal head motion, a stable heartbeat should be isolated in about 15 to 20 seconds. A count-down is shown in the image frame.
• If a large spike in optical intensity is measured in the data (due to motion noise, sudden change in lighting, etc) the data collection process is reset and started over. The sensitivity of this feature can be tweaked by changing data_spike_limit on line 31 of get_pulse.py. Other mutable parameters of the analysis can be changed here as well.

TODO:

• There have been some requests for a youtube video demo
• Instead of processing using the green channel alone, it is likely that some fixed combination of the statistics of the R,G,B channels could instead be optimal (though I was unable to find a simple combination that was better than green alone). If so, the mixing ratios should be determinable from the forward projection matrices of PCA or ICA operators computed on a set of mean value R,G, and B data gathered over a trial data set (and verified with different individuals under different lighting conditions).
• Support for multiple individuals
• Smoother tracking of data from foreheads, perhaps by buffering and registering/inverse-transforming image subframes