very cool

Here frequency is on a vertical axis (on each of 3 plots, low frequency at the bottom, high at the top - from 0 to 488 Hz with ~15 Hz steps), time is on a horizontal (old data on the left overall here is about 10 seconds on the screen). Intensity is encoded with color: blue - low, green - medium, yellow - high, red - even higher.For a reliable gesture recognition, a proper PC processing of these images is required. But for simple activation of robotic hand fingers, it's enough to just use averaged value on 3 channels - uECG conveniently provides it at certain packet bytes so Arduino sketch can parse it.

Control is based on EMG - electrical activity of muscles. EMG signal is obtained by three uECG devices.

https://hackster.imgix.net/uploads/attachments/943624/values_raw_2lGyhS6UFm.png

Here's the code for the arduino: https://www.hackster.io/aka3d6/robotic-hand-control-using-emg-349254#code

It behaves not perfectly and with this processing can only recognize open and closed fingers (and not even each of the 5, it detects only 3 muscle groups: thumb, index and middle together, ring and little fingers together). But "AI" that analyzes signal takes 3 lines of code here and uses a single value from each channel. I believe way more could be done by analyzing 32-bin spectral images on PC or smartphone. Also, this version uses only 3 uECG devices (EMG channels). With more channels it should be possible to recognize really complex patterns - but well, that's the point of the project, to provide some starting point for anyone interested :) Hand control is definitely not the only application for such system.

this window is on top of the schematics...

It can be powered from 9V to 12V.

It may be counter-intuitive, but thumb muscle signal is better visible on the opposite side of the arm, so one of sensors is placed there, and all of them are placed close to the elbow (muscles have most of their body in that area, but you want to check where exactly yours are located - there is quite a big individual difference)

In order to get reasonable readings, it's important to place uECG devices, which are recording muscle activity, in right places. While many different options are possible here, each requires different signal processing approach - so I'm sharing what I've used:

https://hackster.imgix.net/uploads/attachments/943644/p90723-182318_cLuCcIDW1P.jpg