Android Wear Support #69
Replies: 3 comments 8 replies
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There has been some interest in this lately, so I thought I should set out where I think we are with AndroidWear support, and what our options are. I think we have two draft Implementations using different approaches: Analysis on Watch ** Analysis on Phone ** Should we go with one or both of these approaches? Things to consider:
[Note that I know quite a few people are interested in AndroidWear support, so I (Graham) will help include it in the main phone app, but I am not really interested in putting any effort into it myself - I think the main development drive should be in getting a machine learning algorithm fully implemented to reduce the false alarm rate from the system, rather than trying to support more devices, when we know that the low cost PineTime watch works] |
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Certainly,
Our plan is to use the android test harness with real physical data sets as
benchmark.
Also, have I been working on stripping SdServer SdData and SdDataSource
from the android wear and pebble_sd app and moving that into
openseizuredetector openseizuredetector
Then, no discussion afterwards upon changes upstream in pebble_sd /SdData.
Here comes a plus: wear watch becomes osd server.
With that, we can use a ssh connection to cloudflare uid
openseizuredetector openseizuredetector.co.uk for logging and with the same
address as network source display real_time alarms on pebble_sd app.
(this is the nuclear future feature, because it requires a certain
bandwidth. I.e. Fiberglass/4g+)
Met vriendelijke groet,
A. A. F. M. (Bram) Regtien
e: ***@***.***
m: +31614464152
a: Twentehof 13
5709 KM Helmond
Op di 31 mrt 2026, 19:59 schreef Dylan ***@***.***>:
… Thanks @jones139 <https://github.com/jones139> I've not had much time to
look into this in a while. But I'll try to share my experience:
1. Battery was good enough the send interval was increased. Originally
I was sending at 25Hz which drained the battery in a few hours. But
sampling up to 10s or 30s should be fine for a full day.
2. Yes some do but I don't have one. The implementation I have is
fully dependent on the mobile app and I wasn't really interested in
building the complete app on Wear. I also don't know the battery usage for
doing this kind of computation on the watch.
3. Yes we should use the best/tested algorithm. And yeah that's fine
if it's on mobile.
I hope that I can get back to testing the AndroidWear app again soon but I
stopped testing it because it was constantly sending false positives. And I
didn't really get to the bottom of that. I was observing weird cases where
an SMS would be sent but I never even heard it beep (and the beep settings
were on as I heard them sometimes). All that was making it a problem to
test and I didn't really know the codebase well enough to debug why.
Possibly it was just my settings.
With that being said I think the biggest thing I'd want to know to put the
effort into the AndroidWear app is how we can be sure the Machine Learning
algorithm inputs are equivalent for Android Wear and Garmin. If all the
training data is Garmin and there are some quirks that are different I'd be
worried we risk either have false positives or no detection at all. False
positives we can learn about but no detection at all seems to be a worse
problem and I'd assume we need more data to test that. But maybe you have
ideas about how to address this problem?
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Hi Bram,
I don't think you need to change very much in the OSD phone app to achieve
what you want. If you process the data on the watch and send it to the
phone with a flag to make it clear that it is pre-processed, we can handle
it like we do data from the old Pebble watches (which also did on-watch
processing).
Then the SdDataSourceAW on the phone app just needs to read the message,
and determine if it is raw or processed data. If it is raw data, we collate
into a 5 second SdData instance and call doAnalysis() to process it. If it
is processsed data, we will bypass the analysis part and go straight to
generating alarms.
I think the remote data access is a separate issue to AndroidWear isn't
it? It should not matter which data source we are using. Maybe we should
start another discussion about options for that, as we know a couple of
people are using it over VPNs, and I think your idea will avoid the need
for a VPN?
This is about it. Where VPN is good, it is always a struggle with naming
servers.
Re binding through ssh, cloudless to new interface ipv6 at
openseizuredetector.co.uk, we have our automatic twin direction, zero
config data connection. With absolute privacy and ddos protection.
User compartination guaranteed.
In this configuration, group access to user database per account with
anonymised health data sharing enabled will be possible.
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User @AroonPro is working on providing a version of OpenSeizureDetector that can use an Android Wear device instead of a Garmin watch. His version of the watch app is here: https://github.com/AroonPro/AndroidWear_SD/tree/SD_AW/BR
We will probably have a separate companion app to deal with communication with the Android Wear watch, which then sends data to the main OpenSeizureDetector app - the same way that Garmin Connect works for Garmin devices.
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