Tag Archives: Python

Ian Billett: Tracking My Time, My Life

The days I use my time wisely are the days when I feel most fuliflled and therefore happy.

Ian Billett stumbled upon our Quantified Self website here and instantly became fascinated by our community of individuals who were learning about themselves through different technology. With his interest piqued, he began to investigate how he could understand himself. He started with a self-designed Excel spreadsheet where he manually tracked every five minutes using his own tagging system. He’s since switched to even more fine-grained tracking, tagging every minute of his life to describe what he was doing and who he was with. In this talk, presented at the London QS meetup group, Ian describes his process and some of his recent findings.

Slides are also available here.

Special thank you to Ken Snyder for his valuable work documenting the talks at QS London.

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Hands Free Heart Rate Tracking

A quick post here to highlight some interesting developments in the heart rate tracking space. Tracking and understanding heart rate has been a cornerstone of self-tracking since, well since someone put two fingers on their neck and decided to write down how many pulses they felt. We’ve come a long way from that point. If you’re like me tracking heart rate popped up on your radar when you started training for a sporting event like a marathon or long distance cycling. Like many who used the pioneering devices from Polar it felt a bit odd to strap that hard piece of plastic around my chest. After time, and seeing the benefits of tracking heart rate, it became part of my daily ritual. Yet, for all the great things heart rate monitoring can do for physical training, there have been very few advances to provide people with a noninvasive method. That is, until now.

ThearnWebcamPulse

Thearn, an enterprising Github user and developer, has released an open source tool that uses your webcam to detect your pulse. The Webcam Pulse Detector is a python application that uses a variety of tools such as OpenCV (an open source computer vision tool) to “find the location of the user’s face, then isolate the forehead region. Data is collected from this location over time to estimate the user’s heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage’s green channel alone.” If you’re interested in the research that made this work possible check out the amazing work on Eulerian Video Magnification being conducted at MIT. Now, getting it to work is a bit of a hurdle, but it does appear to be working for those who have the technical expertise. If you get it working please let us know in the comments. Hopefully someone comes along that provides a bit of an easier installation solution for those of us who shy away from working in the terminal. Until then, there are actually quite a few mobile applications that use similar technology to detect and track heart rate:

Let us know if you’ve been tracking your heart rate and what you’ve found out. We would love to explore this space together.

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Make Your Own ECG System at Home

Scott Harden, of The Blogging Rotagonist, has created a cheap, functional DIY ECG machine that you can build at home for $1 worth of parts.

He has circuit diagrams, various plots of his data, and the Python code he used for his project in this detailed blog post, along with videos like the one below.

I wonder how many other amazing self-tracking devices could be built from inexpensive, readily available materials?

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