The Pedal Motor Controller (abbreviated PMC) is a project that involves the mechanical turning of guitar pedal knobs for expressive guitar performance. Users can connect their phone over UDP to an Arduino hub that translates their phone's gyroscopic movements into knob turns. The knob turns can then influence music performed with the pedals.
If the user attaches their phone to the guitar headstock, then movements of the guitar will have a direct correlation to sonic output. This is especially relevant for gestural research, as it provides a platform for investigating the relationship between performance gestures and musical style.
- Download the Serial Sensor app on your phone
- Turn on the PMC and connect to its WiFi access point on your phone (SSID and password are both "PedalMotorController")
- In Serial Sensor, under the Sensors tab, select only Gyroscope
- Under the Connection tab, select Network and input the IP address and port shown on the PMC's display
- Press the play button in SerialSensor when you are ready to send data
- Clone this repository to your machine.
- In
src, zip all folders into individual files, EXCLUDINGsrc/Main. - Install the Arduino IDE and launch. Under the Sketch tab, select
Include Library > Add .ZIP Library... - Select and install each of the zipped folders you created in step 2.
- Once all zips are installed, make sure the program compiles by clicking the checkmark on the top left (labeled "Verify").
- Finally, connect an Arduino GIGA to your computer, select it in the top left, and select Upload.
- Arduino GIGA R1 WiFi
- 4x 28BYJ-48 stepper motors
- 4x ULN2003 stepper motor drivers
- LCD1602 2x16 LCD display
- Joystick module
- Mini breadboard
- 3-pin potentiometer
Created by Ian Doherty, fall 2025 through winter 2026, at McGill University's Input Devices and Music Interaction Laboratory (IDMIL).
This project was inspired by similar knob-turning devices, including the Toe-N Control Pedal, the Gecko Tool, and the Knoblin. The ideas underpinning this project are not novel; my sole intent is to explore controlling effect automation using gestures.
Special thanks to Dr. Marcelo Wanderley for his supervision and Darryl Cameron for his assistance in assembly.

