I recently purchased Tinké sensor, and have been using it for the past couple of weeks. The full review will be coming later (I still need to accumulate enough data), but so far I LOVE IT! My first impression is: it is extremely easy to use! Just plug the sensor into your mobile phone, launch the app and tap on the screen. It also provides a lot of interesting metrics, which I hope to use in my self-tracking experiments and projects. Here is a quick breakdown.
I have noticed that a lot of Quantified Self folks and biohackers are interested in longevity research. Personally, I never could understand their fascination with extended life. While I am not completely AGAINST the studies in this area, I regard the whole concept more cautiously and with much less enthusiasm. Lately I have been jotting down some thoughts on this topic, and would like to share them in today’s post. Please note that these are just half-baked thoughts, and I am yet to write a full post (hopefully, sometime in the future).
As you may already know, a couple of weeks ago I attended the 2013 Quantified Self Conference in San Francisco. This was my very first QS conference, and even after two weeks I still feel overwhelmed. The program was full of amazing and inspiring presentations, engaging and thought-provoking “break out” sessions, and demo sessions for cutting edge QS products and services. I also got to meet a lot of interesting like-minded people, including some “movers and shakers” of the QS space and A LOT of readers of Measured Me blog. In today’s post, I will would like to offer a recap of some of the things that I found especially interesting.
As promised, posting today the slides of my “100 Days of Summer” talk that I presented at the 2013 Quantified Self conference in San Francisco last Thursday (October 10). In this presentation, I shared most important and interesting results of the first phase of Measured Me project.
On Monday, September 30, Quantified NYC group has held its 23th meetup. The event was graciously hosted by Projective Space which offers collaborative community space to over 60 startups. With over a hundred people in attendance, interesting demos and inspiring presentations (quantifying Starcraft gaming skills, predicting choice of clothes based on weather forecast, and other self-quantified awesomeness!), it turned out to be a great evening. Here is my brief report on what I saw and loved:
Talking 20 is a young biotechnology start-up in California that aims at making low-cost, at home blood tests that could be used to track twenty essential amino acids (hence the name). In October 2012, I responded to their call for support on Twitter and purchased “T20 Starter Pack” home kit for ten dollars. I paid 12 dollars (2 dollars to cover shipping), and a couple of weeks later received the kit, which I mailed back to them in December. After waiting patiently for ten months, I can finally share with you what I learned from my blood test. Drumroll, please…
In this post, I will discuss three invisible temporal patterns that are likely to be present in your self-tracking data and which, if ignored during analysis, may lead to erroneous conclusions and interpretations. I am talking about trends, social rhythms and intra-day variability.
The Quantified Summer project has finally come to an end last week, and I am currently working on the report that will summarize and present the findings. The actual data will also be made available for download on this blog, and of course, I will share the most interesting results in the future posts. In the meantime, let me explain briefly what exactly this project was about.
Just received my July report from Gmail Meter (check out my post about awesome hacks you can do with Gmail data). As you can see, I am not a big fan of long email responses.
In this post, I would like to share some preliminary findings of my attempts at quantifying and tracking everyday situational and environmental context. Specifically, I will explain how I have been logging everyday situations and environment, and will talk about some interesting patterns that I found in my data.