srlake

Comes from the latest Startup Genome report: "Its startup density is second only to Silicon Valley and a full 50% higher than its closest follower. How can such a small city compete against cities 10 to 30 times their size such as Rio, Atlanta and Rome?"

http://blog.compass.co/waterloo-the-david-vs-goliath-of-star...

Will add the citation to the Medium post as well.

It's been a few years since studying in this area, but one approach we used to use for modelling and control of manipulators (mostly industrial robotic arms) was Screw Theory.

Here's a recent paper using one of these approaches: http://www.intechopen.com/books/international_journal_of_adv...

One approach that could be used for a system like Big Dog would be a feed-forward control loop with kinematic/dynamic modelling of the robot. These approaches use knowledge of the system dynamics to predict the output based on changing inputs or disturbances.

The eye is not focusing on that depth. The light coming out of the lens or LCD would be collimated / diffracted to form an image on the retina that appears to originate from a specific distance (or in this case, many different distances, multiplexed together).

Just like with Google Glass, where the image appears to be at a distance and your eyes do not focus on the actual screen.

What were you hoping to do with the raw EMG data?

Exact same hardware. Difference is software only.

Hi there - good chance we're either getting blocked by a spam filter or have an incorrect email. We've been sending out some kind of email roughly every 4-6 weeks. If you send us a note at thalmic @ thalmic.com we should be able to get that sorted out.

I'm one of the co-founders at Thalmic (Myo).

We get the request for raw muscle data quite often, and are trying to find a way to handle it better in the future. The reason we don't provide it now actually has nothing to do with money (we do have a small number of research groups that pay for this, engineering support, and other services, but it represents a tiny fraction of a percent of revenue).

The reason is actually two-fold:

1. User Experience - Unfortunately you're probably the exception, in that it sounds like you may be able to build a great experience using the raw data. The reality we've found is that it requires a deep machine learning and statistical analysis background to do much useful with the data, considering the need to account for a wide variety of variations in data intra and inter person. What we're trying to prevent is developers attempting to create "custom" gestures improperly (e.g. if (myo.sensor1 > 67% && myo.sensor2 > 30%){output = Rock_On}), which will not work reliably on the population, in different orientations, etc., then the user has a bad experience, and blames the device hardware.

2. Battery Life - Streaming raw data uses much much more power than when we process the data on the device, and recognize gestures directly on-board.

We are considering options to provide this data in some way, as there are certainly good uses in some applications, such as Tennis analysis.

You could quite easily do everything the Smash device does, plus additional information on grip strength, using a Myo armband. It's all about the software!

As a startup-founder (Thalmic, YC w13), I wholeheartedly disagree with the viewpoint of the author here.

Is the pressure high, and the chance of success low? Certainly.

Was the expected value of taking a Google salary rather than the risk-weighted value of starting a company higher? Again, yes.

But the missing piece here is that sometimes it's not about the money, the perks, the hours. I love doing what I'm doing today, regardless of all of the above. My two co-founders here would say the same. We get to choose exactly who we want to work with, what we want to work on, and get a real shot at having an impact on people's lives all over the world, through the products we create.

If we didn't have entrepreneurs taking this irrational leap, we wouldn't have the Google's of the world to employ those who choose the other path.

D-wave is a great example of a company in the class described. Founded in 1999, they didn't ship their first quantum computer until 2011. However, they were able to show progress along the way, solving sub-challenges and as a result were able to raise many rounds of funding before ever shipping a product. http://en.m.wikipedia.org/wiki/D-Wave_Systems

Most startups aren't paying significant taxes in the first year, anyways, since there will be no profits.