Planet Arduino

Brain-to-computer interfaces are nothing new, but they are often difficult to set up or use in more hobby-oriented applications. The team of Ezra Boley and Finn Kuusisto is setting out to change this with their FANTM EMG Arduino Uno shield. It uses a series of conductive pads that pick up electrical signals from a user’s muscles and feeds that data through a set of filters. Once read by the Arduino’s analog input pin, the values are stored within a buffer for later processing. 

The FANTM DEVLPR shield was designed to be open source, letting makers tinker with its layout, capabilities, and hardware. There is a 3mm jack where sensors can be attached, along with a potentiometer for adjusting the gain of the signal. Up to six shields can be stacked and fed into their own analog input pins. The code is open source too and in the form of an Arduino library for easily dropping into whatever application it might be needed. Users can pass their own callback functions via the API that get called when certain events occur, such as a muscle flexing or after an amount of time has transpired. The API also provides basic statistical methods to retrieve average, peak, and latest values. 

To see more about the FANTM DEVLPR Arduino shield, you can visit its website here.

The post The FANTM DEVLPR is an open source electromyography Arduino shield appeared first on Arduino Blog.

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.

[David Nghiem] has been working with circuitry designed to read signals from muscles for many years. After some bad luck with a start-up company, he didn’t give up and kept researching his idea. He has decided to share his innovations with the hacker community in the form of a wearable suit that reads muscle signals.

It turns out that when you flex a muscle, it gives off a signal called a Surface ElectroMyographic signal, or SEMG for short. [David] is using an Arduino, digital potentiometer and a bunch of op amps to read the SEMG signals. LEDs are used to display the signal levels.

The history behind [David’s] project dates back to the late twentieth century, which he eloquently points out – “Holy crap that was a long time ago”. He worked with the MIT Aero Astro Lab and the Boston University Neuromuscular Research Center where he worked on a robotic arm for astronauts. The idea being to apply an opposing force to the arm to help prevent muscle deterioration.

Be sure to check out [David’s] extensive and well documented work, along with the several videos showing his projects at various stages of completion. If this gives you the electromyography bug, check out this guide on detecting the signals and an application of the concept for robotic prosthesis.