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.]

What with wearable tech, haptic feedback, implantable devices, and prosthetic limbs, the boundary between man and machine is getting harder and harder to discern. If you’re going to hack in this space, you’re going to need to know a little about electromyography, or the technique of sensing the electrical signals which make muscles fire. This handy tutorial on using an Arduino to capture EMG signals might be just the thing.

In an article written mainly as a tutorial to other physiatrists, [Dr. George Marzloff] covers some ground that will seem very basic to the seasoned hacker, but there are still valuable tidbits there. His tutorial build centers around a MyoWare Muscle Sensor and an Arduino Uno. The muscle sensor has snap connectors for three foam electrodes of the type used for electrocardiography, and outputs a rectified and integrated waveform that represents the envelope of the electrical signal traveling to a muscle. [Dr. Marzloff]’s simple sketch just reads the analog output of the sensor and lights an LED if it detects a muscle contraction, but the sky’s the limit once you have the basic EMG interface. Prosthetic limbs, wearable devices, diagnostic tools, virtual reality — the possibilities are endless.

We’ve seen a few EMG interfaces before, mainly of the homebrew type like this audio recorder recruited for EMG measurements. And be sure to check out [Bil Herd]’s in-depth discussion of digging EMG signals out of the noise.

Continue reading USB Biofeedback Game Controller lets you play Mario with your guns (video)

USB Biofeedback Game Controller lets you play Mario with your guns (video) originally appeared on Engadget on Fri, 16 Dec 2011 11:07:00 EST. Please see our terms for use of feeds.