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.
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.
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….
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!).
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…