- Android Wear Version of OpenSeizureDetector?
- Errors from Pebble Web Site
- Production Release of V2.3.2
- Update to Beta Version
- Implications of Pebble Sale to Fitbit on OpenSeizureDetector
- New Features in OpenSeizureDetector V2.3.0
- Embrace Watch has Arrived!
- OpenSeizureDetector V2.0.8 Released
- Detection of Short Seizures
- OpenSeizureDetector Reliability Issues?
It uses a Pebble Smart Watch to detect the shaking associated with a seizure, and an Android phone to initiate the alarms. It can raise an alarm in several ways – connecting to the carer’s phone using wifi, via web browser, or by SMS text messages.
The system includes a continuous self testing system to provide reassurance that it is working as intended, and it will raise a fault warning alarm if something goes wrong.
If you are using OpenSeizureDetector, please subscribe to email updates to this site (in the menu bar to the left), or the Facebook page so you can stay up to date with any issues or developments, otherwise there is no way I can get in touch with users of the system if I discover something you should be aware of!
Details of the system and installation instructions are provided on the Pebble Smart Watch Page.
If you have any comments, or queries, please email me at firstname.lastname@example.org
The aim of the OpenSeizureDetector project is to provide a free, Open Source set of tools intended to detect a tonic-clonic epileptic seizure and raise an alarm to alert a parent or carer that someone may need assistance.
It has been developed primarily for our Son, Benjamin, but I hope that the tools will be useful to others, and am happy to support other people in using or developing them. More details can be found on the Purpose of Project and Project Goals pages.rg.uk.
I developed a number of other prototype detectors before settling on the Pebble Smart Watch version. The list of prototypes is:
Pebble Smart Watch (using the accelerometers in the watch).
Microsoft Kinect (using the depth camera to sense movement)
Video Motion Tracking (use video camera to track movement)
Audio (listen for the noises associated with the movement).
There is additional information available on the project github pages.