This library provides an interface to communicate with the Thalmic Lab's Myo, providing the ability to scan for and connect to a nearby Myo armband, and giving access to data from the EMG sensors and the IMU. For Myo firmware v1.0 or higher, access to the output of Thalmic's own gesture recognition is also available.

Both the provided Bluegiga BLED112 dongle (cross-plattfrom) or a standard Bluetooth adapter (Linux) may be used to connect to a Myo armband. The code is primarily developed on Linux.

To install the library simply clone the repository and pip install it:

git clone https://github.com/qtux/myo-raw.git
cd myo-raw
pip install .

To use a native Bluetooth adapter (Linux) you also need to install:

pip install ".[native]"

To run the examples you will also need to install:

pip install ".[emg, classification]"

The myo-raw folder contains the library files to access EMG/IMU data. The Myo communication protocol is implemented in the MyoRaw class.

To use the library, you might need to know the name of the device corresponding to the Myo dongle. The programs will attempt to detect it automatically, but if that doesn't work, here's how to find it out manually:

• Linux: Run the command ls /dev/ttyACM*. One of the names it prints (there will probably only be one) is the device. Try them each if there are multiple, or unplug the dongle and see which one disappears if you run the command again. If you get a permissions error, running sudo usermod -aG dialout $USER will probably fix it.
• Windows: Open Device Manager (run devmgmt.msc) and look under "Ports (COM & LPT)". Find a device whose name includes "Bluegiga". The name you need is in parentheses at the end of the line (it will be "COM" followed by a number).
• Mac: Same as Linux, replacing ttyACM with tty.usb.

To use the libary with a native Bluetooth adapter, you need to consider the three things below. Note that the examples are created using bluez 5.50. There are differences when using older bluez versions.

Power on your Bluetooth adapter manually:

bluetoothctl power on

Or add the following to the /etc/bluetooth/main.conf file:

[Policy]
AutoEnable=true

to ensure automatic power-on after rebooting your computer.

You need to grant raw capturing capabilities for the bluepy-helper, for example by executing:

setcap 'cap_net_raw,cap_net_admin+eip' /usr/lib/python3.6/site-packages/bluepy/bluepy-helper

for a globally installed bluepy.

Set the maximum connection interval to suit your bandwidth requirements. High values require less power but limit the bandwidth. As bluepy has no option for this, you have to edit the /var/lib/bluetooth/$ADAPTER_ADDRESS/$MYO_ADDRESS/info file, which is created upon connecting to the Myo armband with bluez:

bluetoothctl connect $MYO_ADDRESS

Add the following lines (set the MinInterval and MaxInterval values to those that meet your requirements):

[ConnectionParameters]
MinInterval=6
MaxInterval=20
Latency=0
Timeout=200

and restart the Bluetooth service:

systemctl restart bluetooth.service

to apply the parameters. You can use:

btmon | grep interval

to debug the values used during connecting.

To process the data, you can call MyoRaw.add_emg_handler or MyoRaw.add_imu_handler; see examples/emg.py for example reference.

If your Myo has firmware v1.0 or higher, it also performs Thalmic's gesture classification onboard, and returns that information. Use MyoRaw.add_arm_handler and MyoRaw.add_pose_handler. Note that you will need to perform the sync gesture after starting the program (the Myo will vibrate as normal when it is synced).

"Make sure you're wearing Myo with the USB port facing your wrist. Gently flex your wrist away from your body. Myo will begin to vibrate when it recognizes this gesture. Hold this gesture for a few seconds until Myo stops vibrating.

You will know you performed the sync gesture successfully when the Thalmic Labs logo LED on the armband stops pulsing. If it needs to warm up, you will see it blink along with an notification next to the gesture indicator window in Myo Connect. Once Myo is fully warmed up and synced, you will feel three distinct vibrations."

Before running the examples make sure you have the extras requirements installed as described above.

To run an example change directory to the examples folder and execute it with python, e.g. python emg.py.

This example provides a graphical display of EMG readings as they come in. A command-line argument is interpreted as the device name for the dongle; no argument means to auto-detect. You can also press 1, 2, or 3 on the keyboard to make the Myo perform a short, medium, or long vibration.

This example contains a very basic pose classifier that uses the EMG readings. You have to train it yourself: Make up your own poses and assign numbers (0-9) to them. As long as a number key is pressed, the current EMG readings will be recorded as belonging to the pose of that number. Any time a new reading comes in, the program compares it against the stored values to determine which pose it resembles the most. The screen displays the number of samples currently labeled as belonging to each pose, and a histogram displaying the classifications of the last 25 inputs. The most common classification among the last 25 is shown in green and should be taken as the program's best estimate of the current pose.

After you have done some training the Myo class in this file can be used to notify a program each time a pose starts. If run as a standalone script, it will simply print out the pose number each time a new pose is detected. Use Myo.add_raw_pose_handler (rather than add_pose_handler) to be notified of poses from this class's classifier, rather than Thalmic's onboard processing.

• make sure to only press the number keys while the pose is being held, not while your hand is moving to or from the pose
• try moving your hand around a little in the pose while recording data to give the program a more flexible idea of what the pose is
• the rest pose needs to be trained as a pose in itself

This method works fine as long as the Myo is not moved, but it may take quite a large amount of training data to handle different positions well enough.

• on Windows, the readings become more and more delayed as time goes on
• doesn't have access to Thalmic's pose recognition (for firmware < v1.0)
• may or may not work with a Myo that has never been plugged in and set up with Myo Connect
• classify_myo.py segfaults on exit under certain circumstances (probably related to Pygame version)

Thanks to Jeff Rowberg's example bglib implementations, which helped to get started with understanding the protocol.

This project is licensed under the MIT License.