What's so striking to me is that the barriers to prototyping these peripherals for general purpose use has been lowered significantly. It was not long ago where it took days of hacking to connect a Wiimote to your PC, and even then you probably wouldn't get full functionality.
Good work and thanks for sharing!
(also, obligatory "now the NSA can estimate if they can chase you on foot")
Seriously, though, that's because many people have put many hours of work into reverse-engineering protocols and writing libraries, which is fantastic.
This wouldn't be a two-hour job if so many people hadn't open-sourced their work.
Any trained operative can size up someone they are chasing with daily weight data
Also, just ordered a third party version for $16.47 with amazon prime: http://pys.me/Trc
It says that it is compatible with wii fit so I am hoping it uses the same protocol.
Sort of related, the newer WiiFit U coming out Nov 1st will have social features while trying to lose weight with others online http://wiifitu.nintendo.com/
I'm just posting it here because I really[1] like WiiFit and I hope others give it a try, you can truly get a workout from it and lose weight. I hope to make a cool promo vid or something about WiiFit U.
Sort of relatedly, I bought the Withings Wifi scale about a year ago, which does the same thing. I really like it. It is easy to go back and look at weight trends without having to obsess too much about the number each day.
Yeah, exactly. My normal scale was missing the big picture of how my weight fluctuates month-to-month. I do need to implement some smoothing, though, as the raw daily value isn't as useful.
The withings web app has something built in to automatically pipe the data to LoseIt.com, there are several other options as well. I do that because I prefer the interface on LoseIt. You could probably also implement the api for one of those sites if you didn't want to handroll the whole thing.
Daily weigh-ins can be very consistent if you weigh yourself in the morning at about the same time after using the toilet, and before eating/drinking anything.
That's why I want a smoothing algorithm, but the one I found here[1] is giving me a bit of a problem (I can't see why the data points are being squeezed).
Relative to your overall weight, that doesn't seem like it's highly variable, but I've never been much for stats. Try weighing yourself daily after dinner and see the difference.
How much water do you drink per day? I have found that in times where I'm not drinking enough water, my weight will fluctuate more.
Nah, it's not that variable, it just fluctuates a bit. By "it's not as useful" I mean that weight loss/gain happens at larger timescales, so you can't look at normal daily noise and say "omg im so fat".
I think the noise has more to do about the time you last drank water (right before bed, for example), also the time you last ate (the later you eat, the more food you'll still be digesting when weighing yourself). It's not a problem by any stretch, it's just normal data variance.
Worth noting: the author of the Hacker's Diet, John Walker, explains why his choice (exponentially smoothed weighted moving average) is a good choice of smoothing function (vs. some other common functions) for tracking weight over time. See the section "Signal and Noise".
Seconded. I can often predict my weight to 200g if I have a similar diet for a few days running. That said, I have two pairs of scales. Both give reliable results, but one is consistently 500g off from the other. I wouldn't be surprised if most consumer scales have similar levels of inaccuracy. Not a problem for weighing yourself perhaps, but potentially an issue for plane luggage...
This is a great example of innovation at work. And I really like the fact that he removed several dependencies from the existing solutions. You have to love that :)
Yeah, I think they were a bit unnecessary. The original author was using pygame for event notification (a queue would probably be enough) and a thread to receive the events. I just switched it to call methods on a different class, hopefully it will be more useful to people now!
I'm only doing histogramming, so, as long as you don't move a lot, it's pretty accurate. The most frequent value will have 100 samples logged and the second most frequent will have 20, so that's quite the difference. I will compare it to my normal scale when I get back home, but it seems to consistently have a +1kg from that one (a Tanita something).
Good work and thanks for sharing!
(also, obligatory "now the NSA can estimate if they can chase you on foot")