video paceline

Took the first step in making a video paceline.  The raw concept can be viewed.  Current thinking is to concentrate on 5 metre segments and change the speed based on time recorded via the stopwatch or video sensor.  More on the video sensor soon.


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html5 sensorengine – swimming pool ready

Last week I found this fascinating project called Swim Sensing Technology by Joe Marshall.  Researching swimming in cold water inspires a lot of new thinking on using a mobile phone.  Taking the knowledge Joe’s android experiment to extract swimming sensor data from a mobile phone strapped to the body while swimming I have reproduced the data sensor capturing engine using an HTML5 webpage.  The ingredients required:

Code on GITHUB

An android, apple mobile phone   chrome, safari or firefox

A waterproof case (waterproof earphone optional)

Usage:  click start,   go swimming,  end of swim click stop.

Sync back to the cloud and raspberry Pi coming soon.  Data is saved locally on the devices with the sensors.

Feature to come:  voice feedback based on data being analysed in realtime.


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Set up new swimmer and training set

A video guide to adding a swimmer and a swimming training set to the swim train timer.  Get an online / offline stopwatch app at

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Setup of Wireless stopwatch with Swim Train Timer – video

Here is a guide to setting up the wireless stopwatch with a tablet or mobile device using a raspberry pi.


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week 3 multi swimmer week

The last week the swim train timer was simplified to one swimmer.  This week, this was relaxed.  There is no limit to the number of swimmers that can be live in the timer but we kept the numbers to three or four.  Conclusion: it is challenging, both as a time keeper and technology wise.  As a timer keeper, taking splits every 25 metres is a demanding focus of attention.  The swimmers spread out and at some point in time the leader will catch the last swimmer and then we have a conflict on where to look.  And this is the same problem for the logic held within the application.  The application requires order and for that to be kept.  Real swimming life is not like that.  Solutions do exist by automatically tagging the swimmers but that feature is further down the line.


Multi-swimmer swim train timer

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week 2 wireless stopwatch poolside

Thanks to the coaches and swimmers at SilverCityBlues the wireless stopwatch made it through its second week of testing at training.  The learning from being poolside is invaluable.  The tablet made it pool side this week giving the swimmers feedback.


Swim Train Timer -

Some minor bugs to fix and this weeks goal is to handle multiple swimmers analysis and time capture in real time.

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digital wireless stopwatch Cults pool outing

The Digital Wireless stopwatch made it debut at Cults Swimming Pool on Monday evening.  Testing for Wireless and WIFI distance and reception before a training set was recorded.


Swim Train Timer

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Picked up the link to the formation of the Project.  Shares the same spirit as the self engine.  Over the years projects like DataPortablity etc. address the issue of individual data ownership, tools design and ‘knowledge’ derived from the source data.  This is important and the self data we generate only expands.


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data driven, past and future – self engine

The user interface, ie the website or how you view a website on a mobile phone or tablet.  The traintimer and commuication user interfaces were driven by the need to automate the sensor data coming into the software.  That still is a critical element but the data visualisation is primary for the application to bring it to life.

The Self Engine User Interface is under evolution again.  Check out the latest at website.

The goal for this data drive UI is to allow the selection of a swimming stroke & distance and the input of a swimming record.  This will then be present on a chart compared to record times at the world, olymic, masters and hopefully at a club level or any social network that can be built.  The chart will show the history of the record times and also make a suggestion or prediction on where those time will go tomorrow.

As usual the code is open to be contributed to on GITHUB and build status.

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personalised manufacturing – data driven

The collection of Quantified Self data can be an activity for the sake of collection but for most it is for a purpose.  Self improvement, understanding or to make tomorrow better.  And making the future means all things, from yourself to the things you buy, and what can be more personal than clothes.

How can QS data be turned into clothes?  The team at Tribe Sports are showing the way with their Own your market Kicker starter campaign powered by their community.  They focus feedback into their community designer and highlight the economics of being community powered, cheaper.  In terms of design, this means the fit and performance tailoring, even chemicals in the fabric to help with sweat etc.

This is great to see and we will see much more as open manufacturing becomes more pervasive and decentralised, maybe even to our homes and the QS data we collect being directly feedback into future designs for us, e.g. a sweat sensors to the chemical make up of our clothing.

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