Piloton is a Raspberry Pi-based digital assistant for your spin bike. Piloton aims to aggregate and deliver workout-related metrics for Bluetooth-enabled spin bikes and heart rate monitors.
While Piloton is targeting Raspberry Pis, this repository and the scripts in it could be run from any sufficiently-power, Bluetooth-enabled computer that is capable of running Python 3.9.
git clone https://github.com/VSantillo/Piloton.git
Before continuing, ensure that you have Influx DB and Poetry installed.
Install dependencies using Poetry:
poetry install
To run:
poetry run python main.py
Piloton uses InfluxDB (v1.8.4). InfluxDB is a Time Series Database that Piloton uses to record the data points received from your spin bike and heart rate monitor and to report your performance after your workout. For now, this information can be visualized using Grafana.
Piloton uses Poetry. Poetry is a Python dependency management and packaging tool.
Bleak is a Bluetooth Low Energy client that is capable of connecting to devices that broadcast and BLE information. Bleak handles the connection to the bike and the heart rate monitor and receiving that information.
Rich is library that enables rich text and formatting in the terminal. Rich creates the display and handles most of the interactions found in Piloton.
Scikit-learn is library for simple and efficient machine learning in Python. Piloton uses scikit-learn to fit and predict bike Resistance based on an inputs of Cadence, Power, and Speed.
While Piloton is still in development, it's usable for most tinkerers. Ideally, the Piloton would just idly wait until the Schwinn IC4 started broadcasting data packets and then to load into the Workout interface. To ease development, it's in this application state.
When loading into Piloton, you'll be presented with a Menu with 5 options: Workout, User Settings, Device Settings, Training, and Quit
When working out, Piloton will ask you for how long you'd like to workout. Right now, it's indefinite. However, a future goal is to specify a time so that the Piloton will automatically stop recording and break connection with the devices should they no longer be active.
There's 5 metrics on this screen:
Cadence: Average RPM of flywheel, read directly from bike
Resistance: Dimensionless value that reflects how much effort it takes to spin the flywheel, predicted from Cadence, Power and Speed
Power: Power in Watts, read directly from bike
Heart Rate: Heart Rate, read directly from heart rate monitor. Heart Zones are based off of established thresholds determined by maximum heart rate by age. Age can be adjusted in User Settings.
Power Zone: Power Zone, interpreted from bike power output. Power Zones are based off of established threshold determined by Functional Threshold Power (FTP), or the average power output over an hour. FTP can be adjusted in User Settings.
When you want to end a workout, press Ctrl+C.
A desired goal of Piloton is that it should be able to be bike agnostic. The Schwinn IC4 can have wildly varying factory calibrations, so I wanted to provide a mechanism that would allow for anyone to train their own resistance scale on their own bike. You may retrain the resistance scale however you'd like.
When you start training, it will ask you for what resistance you'd like to train on. After providing a value, the Piloton will load up a grid of bars that show how many samples have been collected for that given resistance and each respective cadence. These values will get fit a Classifier and then predicted during a workout. You will need to train on as many values of resistance as you want to see.
When you want to end training on a specific cadence, press Ctrl+C
I recognize that this process is long and tedious. I don't really know much about machine learning and will be the first to admit this is the weakest component of the whole project. I will include a full data set collected from my Schwinn IC4 as I develop more on this project. My immediate goals with this training process is to make it faster and to make it more accurate.
You can update the bike name and HRM name in the settings. Right now, Piloton assumes you will always have a bike and an HRM whenever doing a workout. These names should be the names that broadcast from those devices.
A few months ago, I purchased a Schwinn IC4 spin bike because it connects to the Peloton digital app (via Bluetooth) that I would run from my phone. This experience is certainly adequate, however, the bike's display doesn't really provide much information that can help me make decisions about progressions during my workout. A Redditor (/u/zomrise) posted their Schwinn-to-Peloton Resistance Converter which piqued my interest in what information could be received from the Schwinn IC4. There are apps like Kinetic that provide data collection and visualization during a workout, however, when you're doing a Peloton digital class on the phone, only Kinetic or Peloton can be on screen. My approach was to consider Piloton as a pseudo-replacement for the Schwinn IC4 display. However, I felt that there was a way to add value such that this information could be aggregated on any Bluetooth enabled bike.
Developing Piloton also allowed me to work with some new-to-me technology that I just hadn't found an opportunity to work with in a personal project. This is my first Raspberry Pi-based project and my first time doing any Bluetooth work. There's a very small "Machine Learning" component which is a Decision Tree Classifier for right now. I'm hoping to take a few of the things I've learned here, find a few collaborators, and really put some polish into this project.