Alternative Operating System (Cyanogenmod) on Samsung S4 mini

My Samsung S4 mini Android mobile phone works very well, but it keeps running out of internal storage space for applications, so in practice I can not have very many of my own applications on the device.

I realised this is because the phone came with a lot of applications pre-installed, which keep getting updated, and the updates take up storage space (in addition to the factory installed version, which is not replaced).   And I don't use most of the applications that are installed on it - no need for things like Google Maps when you can use OsmAnd navigation etc. whcih uses OpenStreetMap data so is more detailed.

So tonight I decided to try installing cyanogenmod, which is another build of Android that can replace the factory firmware.    I found this a bit nerve wracking because I was doing it as a bit of a 'black box' - download this file, press these buttons etc.   There are also several versions of a S4 mini (mine is a GT-I9192, which seems to be less common).   If I were doing it on a Windows computer I would be very worried about viruses etc. - still nervous about the firmware that I have downloaded - might try to build it from source another day to give me a bit more confidence.

The end result is my phone seems to work, running cyanogenmod 11, which is good

Don't treat this as instructions of how to do it - it is just my notes so I can remember.

Recovery Image
The S4 mini has a recovery mode, which seems to be a very small operating system.   You need a replacement for this which will let you do more  things (like backup your existing firmware before you start anything more serious).
There are a few different alternative recovery systems around, but the one I found that claims to work on an I9192, is called 'Philz' which is a more advanced version of one called 'clockworkmod'.

I got the latest version of Philz recovery from the link here.    And loaded it onto the device using the 'heimdal' software running on my xubuntu linux laptop (I just used the ubuntu packaged version rather than building from source) - I did this by following the instructions here.

It is now possible to boot the phone into recovery mode by pressing the Volumme Up, Home and Power buttons when booting.

Install Cyanogenmod
The extra worrying part is that you need the version of cyanogenmod that matches your phone (not sure what will happen if you don't, but it might take a bit of recovering from...).   I searched the internet to find an unofficial version for my phone (GT-I9192), and got the latest version from here, which is referenced from a post on the xda developers forum.

This went surprisingly smoothly - you can set the recovery program to install a 'zip' image from sideloader, and send the image using 'adb sideload '.

Re-booted and the phone works again, phew!.

Google Apps
One issue with the 'stock' cyanogenmod is that it does not include any of the propriatory google applications, in particular I wanted GMail, Google Plus and the play store.
While it is possible to back them up from the factory firmware, and then restore them into cyanogenmod, you can get pre-packaged versions on the internet (may be issues with licencing here I suspect...), which are packaged as 'gapps' and can be loaded as a 'zip' file the same way as cyanogenmod.

This now gives me a working gmail etc., and i can install other apps like osmand, national rail etc. using play store.

Unfortunately I have installed loads of other google apps that I don't really want, which slightly defeats the object of  going to an alternative firmware - I might have to look at doing the backup and restore bit myself and being more selective about what I back up....

So, I think I have got back to a working phone - I'll have to test it a bit this week before I go travelling again and need it more.

Charity Document Management System

After a bit more development of the Document Management System for our Academy Charitable Trust (HDMS), I have now got something working which I think is useable.    There may well be some changes once we use it in anger for a while and find some 'features' annoying!

Background

HDMS is a Document Management System that has been developed for Hartlepool Aspire Trust (Catcote Academy).

It has been developed because the Trust is expected to have many policies to ensure compliance with statutory regulations, and these policies are implemented within the trust using procedures for detailed instructions, and forms to record information.

It is important that the latest versions of the Policies, Procedures and Forms are available to staff and key stakeholders, and that changes between versions can be tracked and communicated to stakeholders so they know what has changed when a new document is issued.

User Interface

HDMS has been developed to store the Trust's documents in a single repository (a web server) and present the latest version of documents to interested parties. Users are initially presented with a graphical summary of the document structure.
 The user clicks on parts of the graphical summary to search for specific types of documents (such as Financial Procedures, or Human Resources Policies). This gives a list of documents, showing the latest revision number with date of issue, with clickable icons to download either the PDF version or native version of the file.
Authorised users have options to create new revisions, or edit existing draft documents.

Draft versions of documents are not publicly visible, but can be viewed by authorised users. Approval and issue of documents is managed by the draft document being sent electronically to reviewers/approvers.
The document is issued and becomes the latest version once all the reviewers/approvers have approved the document.

The workflow for creating, revising and approving a document is shown in a set of slides here.

The system stores both 'native' (e.g. MS Word) documents and PDF documents. By default the PDF version is delivered to the public, as this can not be modified accidentally. The system can also store 'extra' files, which may be the source files for drawings or tables of data that are used in the document - this is useful for future updates so the author can obtain all the data used to produce the original document.

Live Version

The live version of the system is running at (http://catcotegb.co.uk/hdms).

The software is quite general so may be of use to other small and medium size organisations who wish to manage their documentation in a systematic way. There is a demonstration version of the system available for testing at http://catcotegb.co.uk/hdms_demo - login as 'user1' with password 'test').   The source code is available on my GitHub repository.

Please let me know if you are interested in using this for your organisation and I will help explain how to set it up, because my installation instructions may not be complete!

Academy Charitable Trust Document Management System

Last year our school converted to an academy.   To help us with the set-up of the administrative side of the new organisation, I set up an electronic document management system to hold our management documents such as policies and procedures.

The system I set up was a modified version of OpenDocMan.   This has worked pretty well from the point of view of recording the documents and allowing us to retrieve the issued version, but now we are looking at updating some of the documents, and establishing another part of the organisation, we are finding some limitations.   The most significant problem is that the document does not appear publicly while it is waiting for approval - I want the latest issued document to always be available even while we are reviewing and approving the new version.

I decided that rather than modifying my version of OpenDocMan, it is probably better to write an alternative simple system based on an established software framework.

The new Hartlepool Aspire Trust Document Management System (HDMS) is based on the cakephp framework, which makes interfacing with the database, and dealing with internet http requests very simple, and it automatically produced the code to do basic database record creation/deletion etc. automatically, so I only had to do the 'business' logic.

The concepts for the new system and workflow are shown in these slides, and there is a demo installation here.

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.