This one day event came out of the now well-established London QS meetups. An opportunity to draw out some ideas about areas the group can pursue. Thanks as always to Adriana, Ken, guest speakers and others for putting this together.
I’m just reporting here on one openspace session. For the rest please see the London QS website and quantifiedself.com.
@rainycat and I initiated a discussion on what Open Source approaches can do for QS. This could be hardware, software or a combination of both addressing any of the elements of QS solutions nameless:
- sensors and data capture
- data communications and storage
- data analysis and visualisation
We discussed why we like open sources so much: community, sharing, “the right to repair” etc. In some cases we end up with better quality solutions than commercial equivalents, Apache being one classic example.
In early stage markets it’s usually not possible to launch a commercial solution when the problem is not well-defined and the size of audience uncertain. However, by sharing the task of building and testing prototypes the state of the art can be advanced.
We looked at a possible wish-list for open source solutions. The immediate suggestion was to produce tools for analysing data from established commercial products such as
- Zeo (event sponsor)
- Jawbone Up
- Polar Wearlink etc (see QS guide)
At this point we ran out of time for discussion. The above off-the-shelf devices have the sensors and some of the communication, data storage and visualisation which leaves me with a question about what we need to do with these? Based on Adriana’s input I think we are talking about getting the data out and presenting it in different ways. Rain and I are equally interested in the sensors themselves. To be further discussed.
The next step is to formulate a project. However, it’s very important not to waste effort and momentum on re-inventing any wheels as significant effort may be needed regardless of what we choose to do. It’s also important to go after challenges that are both important and useful. Boiling the whole ocean is not an option without significant funding so we need to start with a specific and achievable objective and build on it from there.
I therefore proposed a survey of the London group to establish what should be addressed first. I will put this out in another blog or poll shortly. This will be about priorities and potential “quick wins”.
PS. As I am personally interested in heart-rate monitoring at the moment I googled around to see what’s cooking in the open source community in relation to this. I was pleasantly surprised to see that, if we wanted to attack this area, we would certainly not be starting from scratch. This confirms my theory that QS and Open Source go well together and that, with a bit of research we may find that some of our challenges are already being addressed.
PS. As Adriana noted: There may be loads of s/w for data analysis and visualisation but is it usable and user-friendly for people who have no software expertise? I found even the simple hack to extract my data from FitBit difficult…. And if they are usable and easy to use, where would we begin to look for them? This suggests that cataloguing the existing stuff may be one of the priority tasks. Based on that we can look at usability by QS people who are not IT specialists.