11–20 of 31 posts
Actually thought about an app that would do this on a smartwatch. I would definitely buy a smartwatch if I could have this level of accuracy of body movement tracking.
WatchOS can detect the type of exercise, based on a recent talk I saw. Here's the docs on the types of exercises detected: https://developer.apple.com/library/watchos/documentation/He...
Actually thought about an app that would do this on a smartwatch. I would definitely buy a smartwatch if I could have this level of accuracy of body movement tracking.
This is fantastic! Do you think it's feasible to develop this into a 1 phone solution? I can see a lot of applications in the rehab sciences area.
Here's a problem: I'm pretty sure you'd get just as good (probably superior ) results with a simpler, faster out-of-the-box algorithm like logistic regression, random forests or boosted trees (depending on the amount of data). This just isn't a problem that seems well suited for neural networks. Gyroscope/accelerometer data might seem like the kind of extremely-noisy, high-dimensional data well-suited for neural nets…
If I had tons of data (a lot of different people using it), I'd experiment with LSTM neural networks because I think the temporal information is crucial for determining a movement.
I think a killer app would be aimed at weight lifters. If you go to the gym, the serious weight lifters record their reps and weight for each exercise. The app would utilize the iWatch or some other wearable and detect the exercise and count the reps. Then it would prompt the user for how much weight they used.
I can find apps for recording training data, but they all use their own formats. Anyway, if there is one it would be cool if this program could use it as output, instead of making up its own ad-hoc format.
Here's a problem: I'm pretty sure you'd get just as good (probably superior ) results with a simpler, faster out-of-the-box algorithm like logistic regression, random forests or boosted trees (depending on the amount of data). This just isn't a problem that seems well suited for neural networks. Gyroscope/accelerometer data might seem like the kind of extremely-noisy, high-dimensional data well-suited for neural nets…
The system is designed to be personalised. So for example if it's not doing well in recognising your squats for instance, you can immediately 'show' it what they look like. A neural network can immediately integrate that, and spit out great accuracy levels. Whilst i'd have to reconstruct a decision tree each time.
Here's a problem: I'm pretty sure you'd get just as good (probably superior ) results with a simpler, faster out-of-the-box algorithm like logistic regression, random forests or boosted trees (depending on the amount of data). This just isn't a problem that seems well suited for neural networks. Gyroscope/accelerometer data might seem like the kind of extremely-noisy, high-dimensional data well-suited for neural nets…
I agree and disagree. I would definitely start with a simpler algorithm like logistic regression or even trees and use temporal features like power at different frequencies by taking an FFT. I would even add time lagged features. After that I'd graduate to a single hidden layer MLP. If I had tons of data (a lot of different people using it), I'd experiment with LSTM neural networks because I think the temporal inform…
The repetition counting in my next aim. Once apple opens up the gyro on the Watch, I think there could be a good chance to get something like this out the door.