Thanks for taking the time to look at my Wolfram Summer Camp project - I hope you enjoyed it.

A few notes:

1. The device I'm using here is an Android phone - I did develop a platform-agnostic Channel[]-based method for sensor data collection detailed in Part 1 (http://community.wolfram.com/groups/-/m/t/1386358), though, and there is some documentation for doing a similar thing with iDevices.
2. When you refer to 'interpreting gestures', are you referring to accelerometer-based / air gestures or touch gestures (Googling 'gesture recognition yields many results for the latter and few for the former)? If Android / Apple have released a library for accelerometer-based gestures, do post the link here - I'd be interested to see it. (n.b. I'm using accelerometer sensors, not GPS)
3. Your sound wave based suggestion sounds quite interesting - I might take a look at Mathematica's sound processing capabilities (e.g. this seems interesting for determining when a gesture starts/stops: https://www.wolfram.com/language/11/computational-audio/transient-detection.html).
4. I'm not completely certain, however, as to the viability of a non-'computer learning' / machine learning approach to waveform classification - even if I converted the data to sound waves, as I understand it I would still have to use some machine learning model to classify the waveforms (time series classification --> e.g. hidden Markov models, or recurrent neural networks, both of which would also work with the raw accelerometer data simply presented as a time series). Again, if there's something the Wolfram Language has (built-in or otherwise) that could help with this, please do let me know.

Kind regards,

Euan