Breathing Detection with Kinect – A working Prototype Seizure Detector!

The seizure detector project has come forward a long way since I have been using the Kinect.
I now have a working prototype that monitors breathing and can alarm if the breathing rate is abnormally low.   It sends data to our ‘bentv’ monitors (image right), and has a web interface so I can see what it is doing (image below).   It is on soak test now…..

Details at http://openseizuredetector.org.uk.

Breathing Detection using Kinect and OpenCV – Part 2 – Peak detection

A few days ago I published a post about how I am using a Microsoft Kinect depth camera and the OpenCV image processing library to identify a test subject from a background, and analyse the series of images from the camera to detect small movements.

The next stage is to calculate the brightness of the test subject at each frame, and turn that into a time series so we can see how it changes with time, and analyse it to detect specific events.

We can use the openCV ‘mean’ function to work out the average brightness of the test image easily, then just add it onto the end of an array, and trim the first value off the start to keep the length the same.
The resulting image and time series are shown below:


 The image here shows that we can extract the subject from the background quite accurately (this is Benjamin’s body and legs as he lies on the floor).  the shading is the movement relative to the average position.

The resulting time series is shown here – the measured data is the blue spiky line.  The red one is the smoothed version (I know I have a half second offset between the two…).

The red dots are peaks detected using a very simple peak searching algorithm.
The chart clearly shows a ‘fidget’ being detected as a large peak.  There is a breathing event at about 8 seconds that has been detected too.

So, the detection system is looking promising – I have had better breathing detection when I was testing it on myself – I think I will have to change the position of the camera a bit to improve sensitivity.

I have now set up a simple python based web server to allow other applications to connect to this one to request the data.

We are getting there.  The outstanding issues are:

  • Memory Leak – after the application has run for 30 min the computer gets very slow and eventually crashes – I suspect a memory leak somewhere – this will have to be fixed!
  • Optimum camera position – I think I can get better breathing detection sensitivity by altering the camera position – will have to experiment a bit.
  • Add some code to identify whether we are looking at Benjamin or just noise – at the moment I analyse the largest bright subject in the image, and assume that is Benjamin – I should probably have a minimum size limit so it gives up if it can not see Benjamin.
  • Summarise what we are seeing automatically – “normal breathing”, “can’t see Benjamin”, “abnormal breathing”, “fidgeting” etc.
  • Modify our monitors that we use to keep an eye on Benjamin to talk to the new web server and display the status messages and raise an alarm if necessary.

The code is available here.

Breathing Detection using Kinect and OpenCV – Part 1 – Image Processing

I have had a go at detecting breathing using an XBox Kinnect depth sensor and the OpenCV image processing library.
I have seen a research paper that did breathing detection, but it relied on fitting the output of the Kinect to a skeleton model to identify the chest area to monitor.  I would like to do it with a less calculation intensive route, so am trying to just use image processing.

To detect the small movements of the chest during breathing, I am doing the following:

Start with a background depth image of empty room.

Grab a depth image from kinect
Subtract Background so we have only the test subject.
Subtract a rolling average background image, and amplify the resulting small differences – makes image very sensitive to small movements.
Resulting video shows image brightness changing due to chest movements from breathing.
We can calculate the average brightness of the test subject image – the value clearly changes due to breathing movements – job for tomorrow night is to do some statistics to work out the breathing rate from this data.
The source code of the python script that does this is the ‘benfinder’ program in the OpenSeizureDetector archive.

    A Microsoft Kinect Based Seizure Detector?

    Background

    I have been trying to develop an epileptic seizure detector for our son on-and-off for the last year.   The difficulty is that it has to be non-contact as he is autistic and will not tolerate any contact sensors, and would not lie on a sensor mat etc.
    I had a go at a video based version previously, but struggled with a lot of noise, so put it on hold.

    At the weekend I read a book “OpenCV Computer Vision with Python” by Joseph Howse – this was a really good summary of how to combine openCV video processing into an application – dealing with separating user interface from video processing etc.   Most significantly he pointed out that it is now quite easy to use a Microsoft Kinect sensor with openCV (it looked rather complicated earier in the year when I looked), so thought I should give it a go.

    Connecting Kinect

    When I saw a Kinect sensor in a second hand gadgets shop on Sunday, I had to buy it and see what it can do.

    The first pleasant surprise that I got was that it came with a power supply and had a standard USB plug on it (I thought I would have to solder a USB plug onto it) – I plugged it into my laptop (Xubuntu 13.10), and it was immediately detected as a Video4Linux webcam – a very good start.

    System Software

    I installed the libfreenect library and its python bindings (I built it from source, but I don’t think I had to – there is an ubuntu package python-freenect which would have done it).

    I deviated from the advice in the book here, because the Author suggested using the OpenNI library, but this didn’t seem to work – looks like they no longer support Microsoft Kinect sensors (suspect it is a licensing issue…).   Also the particularly clever software to do skeleton detection (Nite) is not open source so you have to install it as a binary package, which I do not like.   It seems that the way to get OpenNI working with Kinect is to use a wrapper around libfreenect, so I decided to stick with libfreenect.

    The only odd thing is whether you need to be root to use the kinect or not – sometimes it seems I need to access it as root, then after that it works as a normal user – will think about this later – must be something to do with udev rules, so not a big deal at the moment….

    BenFinder Software

    To see whether the Kinect looks promising to use as a seizure detector, wrote a small application based on the framework in Joseph Howse’s book.   I had to modify it to work with libfreenect – basically it is a custom frame grabber.
    The code does the following:
    • Display video streams from kinect, from either the video camera or the infrared depth camera on the kinect – works!  (switch between the two with the ‘d’ key).
    • Save an image to disk (‘s’ key).
    • Subtract a background image from the current image, and display the resulting image (‘b’ key).
    • Record a video (tab key).
    The idea is that it should be able to distinguish Benjamin from the background reliably, so we can then start to analyse his image to see if his movements seem odd (those who know Benjamin will know that ‘odd’ is a bit difficult to define for him!).

    Output

    I am very pleased with the output – it looks like it could work – a few images:

    Output from Kinect Video Camera (note the clutter to make detection difficult!)
    Kinect Depth Camera Output – Note black hole created by open door.

    Depth Camera Output with background image subtracted – note that the subject stands out quite clearly.
    Example of me trying to do Benjamin-like behaviours to see if I can be detected.

    Conclusion & What Next

    Background subtraction from the depth camera makes the test subject stand out nice and clearly – should be quite easy to detect him computationally.
    Next stage is to see if the depth camera is sensitive enough to detect breathing (when lying still) – will try by subtracting an each image from the average of the last 30 or so, and amplifying the differences to see if it can be seen.
    If that fails, I will look at Skeltrack to fit a body model to the images and analyse movement of limbs (but this will be much more computationally costly).
    Then I will have to look at infrastructure to deploy this – I will either need a powerful computer in Benjamin’s room to interface with the Kinect and do the analysis, or maybe use a Raspberry Pi to interface with the kinect and serve the depth camera output as a video stream.
    Looking promising – will add another post with the breathing analysis in the new year…