We don’t see many EMG (electromyography) projects, despite how cool the applications can be. This may be because of technical difficulties with seeing the tiny muscular electrical signals amongst the noise, it could be the difficulty of interpreting any signal you do find. Regardless, [hut] has been striving forwards with a stream of prototypes, culminating in the aptly named ‘Prototype 8’

The current prototype uses a main power board hosting an Arduino Nano 33 BLE Sense, as well as a boost converter to pump up the AAA battery to provide 5 volts for the Arduino and a selection of connected EMG amplifier units. The EMG sensor is based around the INA128 instrumentation amplifier, in a pretty straightforward configuration. The EMG samples along with data from the IMU on the Nano 33 BLE Sense, are passed along to a connected PC via Bluetooth, running the PsyLink software stack. This is based on Python, using the BLE-GATT library for BT comms, PynPut handing the PC input devices (to emit keyboard and mouse events) and tensorflow for the machine learning side of things. The idea is to use machine learning from the EMG data to associate with a specific user interface event (such as a keypress) and with a little training, be able to play games on the PC with just hand/arm gestures. IMU data are used to augment this, but in this demo, that’s not totally clear.

All hardware and software can be found on the project codeberg page, which did make us double-take as to why GnuRadio was being used, but thinking about it, it’s really good for signal processing and visualization. What a good idea!

Obviously there are many other use cases for such a EMG controlled input device, but who doesn’t want to play Mario Kart, you know, for science?

Checkout the demo video (embedded below) and you can see for yourself, just be aware that this is streaming from peertube, so the video might be a little choppy depending on your local peers. Finally, if Mastodon is your cup of tea, here’s the link for that. Earlier projects have attempted to dip into EMG before, like this Bioamp board from Upside Down Labs. Also we dug out an earlier tutorial on the subject by our own [Bil Herd.]

To measure how fast something spins, most of us will reach for a tachometer without thinking much about how it works. Tachometers are often found in cars to measure engine RPM, but handheld units can be used for measuring the speed of rotation for other things as well. While some have mechanical shafts that must make physical contact with whatever you’re trying to measure, [electronoobs] has created a contactless tachometer that uses infrared light to take RPM measurements instead.

The tool uses an infrared emitter/detector pair along with an op amp to sense revolution speed. The signal from the IR detector is passed through an op amp in order to improve the quality of the signal and then that is fed into an Arduino. The device also features an OLED screen and a fine-tuning potentiometer all within its own self-contained, 3D-printed case and is powered by a 9 V battery, and can measure up to 10,000 RPM.

The only downside to this design is that a piece of white tape needs to be applied to the subject in order to get the IR detector to work properly, but this is an acceptable tradeoff for not having to make physical contact with a high-speed rotating shaft. All of the schematics and G code are available on the project site too if you want to build your own, and if you’re curious as to what other tools Arduinos have been used in be sure to check out the Arduino-based precision jig.